Systems and methods for determining respiration information from a photoplethysmograph

ABSTRACT

A patient monitoring system may determine one or more reference points of a physiological signal. The system may select one or more fiducial points on the physiological signal relative to the reference points. The one or more fiducial points may be selected by selecting a point spaced by a time interval relative to one of the reference points. The time interval may be a predetermined constant, or the time interval may depend on physiological information. The system may generate a fiducial signal based on the selected fiducial points, calculate physiological information such as a respiration rate based on the selected fiducial points, or both.

The present disclosure relates to physiological signal processing, andmore particularly relates to extracting respiratory information from aphotoplethysmograph signal.

SUMMARY

A patient monitoring system may be configured to determine physiologicalinformation from a physiological signal using a suitable combination ofone or more reference points in the physiological signal and one or morefiducial points in the physiological signal. A reference point may bedetermined by performing mathematical calculations on the physiologicalsignal to find minima, maxima, zeros or other points of a physiologicalsignal or signal derived thereof (e.g., derivatives, integrals). Afiducial point may be used to calculate physiological information,signal metrics, or other information. The patient monitoring system maydetermine a reference point on a sampled physiological signal, and thendetermine a fiducial point on the sampled physiological signal based atleast in part on the reference point and based at least in part on atime interval relative to the reference point. For example, the patientmonitoring system may select a set of fiducial points located 210milliseconds from a set of respective local maxima of the firstderivative of a photoplethysmograph signal (e.g., maxima of the firstderivative of each pulse wave), and determine a respiration rate basedon the set of fiducial points. The time difference may be apre-determined value, or may depend on physiological information such asan instantaneous pulse rate. The patient monitoring system may create afiducial signal based at least in part on determined fiducial points,and may determine physiological information based at least in part onthe newly created signal.

In some embodiments, a patient monitoring system may locate twosuccessive reference points corresponding to two successive pulse wavesof a sampled photoplethysmograph signal. The patient monitoring systemmay then locate a maximum value of the first derivative of the sampledsignal between the two successive reference points. Using the locationof the maximum value as a further reference point, the patientmonitoring system may select a fiducial point located a particular timeinterval (or corresponding number of samples) before or after themaximum value. Based on the fiducial points, the patient monitoringsystem may determine respiratory information such as, for example, arespiration rate. In some embodiments, the fiducial point may be locateda predetermined time interval (or corresponding number of samples) awayfrom the maximum value. In some embodiments, the particular timeinterval (or corresponding number of samples) is based at least in parton physiological information such as an average heart rate. For example,the particular time interval (or corresponding number of samples) may be10% of the pulse period of the averaged heart rate (i.e., about 100milliseconds corresponding to a 60 BPM averaged heart rate).

BRIEF DESCRIPTION OF THE FIGURES

The above and other features of the present disclosure, its nature andvarious advantages will be more apparent upon consideration of thefollowing detailed description, taken in conjunction with theaccompanying drawings in which:

FIG. 1 shows an illustrative patient monitoring system in accordancewith some embodiments of the present disclosure;

FIG. 2 is a block diagram of the illustrative patient monitoring systemof FIG. 1 coupled to a patient in accordance with some embodiments ofthe present disclosure;

FIG. 3 shows a block diagram of an illustrative signal processing systemin accordance with some embodiments of the present disclosure;

FIG. 4 shows an illustrative PPG signal that may be analyzed inaccordance with some embodiments of the present disclosure;

FIG. 5 shows an illustrative signal that may be analyzed in accordancewith some embodiments of the present disclosure;

FIG. 6 shows the illustrative signal of FIG. 5 including illustrativefiducial points in accordance with some embodiments of the presentdisclosure;

FIG. 7 shows illustrative graphs depicting a PPG signal from whichfiducial points may be derived in accordance with some embodiments ofthe present disclosure;

FIG. 8 shows illustrative graphs depicting a PPG signal from whichreference points and fiducial points may be derived in accordance withsome embodiments of the present disclosure;

FIG. 9 is flow diagram showing illustrative steps for determiningphysiological information in accordance with some embodiments of thepresent disclosure;

FIG. 10 is flow diagram showing illustrative steps for determiningrespiration information in accordance with some embodiments of thepresent disclosure;

FIG. 11 is flow diagram showing illustrative steps for generating afiducial signal from a physiological signal in accordance with someembodiments of the present disclosure;

FIG. 12 is flow diagram showing illustrative steps for analyzingfiducial signals generated according to the steps of, for example, FIG.11 in accordance with some embodiments of the present disclosure; and

FIG. 13 shows a chart of an illustrative comparison for various fiducialpoint selections in accordance with some embodiments of the presentdisclosure.

FIG. 14 shows an illustrative PPG signal having morphologycharacteristics relating to respiration in accordance with someembodiments of the present disclosure;

FIG. 15 illustrates an effect of respiration on a PPG signal inaccordance with some embodiments of the present disclosure;

FIG. 16 shows an illustrative PPG signal, a first derivative of the PPGsignal, and a second derivative of the PPG signal in accordance withsome embodiments of the present disclosure;

FIG. 17 shows an illustrative amplitude modulated PPG signal inaccordance with some embodiments of the present disclosure;

FIG. 18 shows an illustrative baseline and amplitude modulated PPGsignal in accordance with some embodiments of the present disclosure;

FIG. 19 is flow diagram showing illustrative steps for generatingmorphology metric signals from a PPG signal in accordance with someembodiments of the present disclosure;

FIG. 20 shows a series of graphs illustrating how a down metric signalmay be generated from a PPG signal in accordance with some embodimentsof the present disclosure;

FIG. 21 is a flow diagram showing illustrative steps for determiningwhich portions of the analysis window include useable data in accordancewith some embodiment of the present disclosure;

FIGS. 22A and 22B is a flow diagram showing illustrative steps forgenerating respiration information utilizing autocorrelation ofmorphology metric signals in accordance with some embodiments of thepresent disclosure;

FIG. 23 depicts aspects of determining an illustrative autocorrelationmetric from an autocorrelation sequence in accordance with someembodiments of the present disclosure;

FIG. 24 is a flow diagram showing illustrative steps for generating ascalogram from a combined autocorrelation sequence in accordance withsome embodiments of the present disclosure;

FIG. 25 depicts cyclical padding of a combined autocorrelation sequencein accordance with some embodiments of the present disclosure;

FIG. 26 depicts convolution of a padded combined autocorrelationsequence with a mother wavelet in accordance with some embodiment of thepresent disclosure;

FIG. 27 is a flow diagram showing illustrative steps for derivingrespiration information from a sum scalogram vector in accordance withsome embodiments of the present disclosure;

FIG. 28 is a flow diagram showing illustrative steps for derivingrespiration information from a combined autocorrelation sequence inaccordance with some embodiments of the present disclosure;

FIG. 29 shows a graph illustrating analysis of a combinedautocorrelation sequence in accordance with some embodiments of thepresent disclosure;

FIG. 30 shows a graph illustrating analysis of a combinedautocorrelation sequence having limited respiration information inaccordance with some embodiments of the present disclosure; and

FIG. 31 shows a graph illustrating analysis of a combinedautocorrelation sequence having harmonics in accordance with someembodiments of the present disclosure.

DETAILED DESCRIPTION OF THE FIGURES

The present disclosure is directed towards determining respirationinformation from a physiological signal. A patient monitoring system mayreceive one or more physiological signals, such as a photoplethysmograph(PPG) signal generated by a pulse oximeter, from a sensor coupled to apatient. The patient monitoring system may condition (e.g., amplify,filter, sample, digitize) physiological signals received from thesensor, perform suitable mathematical calculations on the conditionedsignals to locate reference points, and determine one or more fiducialpoints of the conditioned signal.

Fiducial-defined portions may be determined based on the fiducialpoints. In some embodiments, suitable mathematical calculations may beperformed on the fiducial defined portions of the physiological signalto obtain one or more morphology metrics, such as a down metric, akurtosis metric, and a delta of second derivative (DSD) metric. Aninterpolated signal may be generated for each of the morphology metricsto generate a down metric signal, a kurtosis metric signal, and a DSDmetric signal.

An autocorrelation may be performed on each morphology metric signal togenerate one or more autocorrelation sequences, e.g., to indicate theregularity or periodicity of the morphology metric signals. Theautocorrelation sequences may be combined based on the autocorrelationmetrics to generate a combined autocorrelation sequence.

The autocorrelation sequence may be used to determine respirationinformation such as respiration rate. In one exemplary embodiment therespiration information may be determined from the autocorrelationsequence. In another exemplary embodiment a wavelet transform may beutilized to determine the respiration information. The system mayperform a convolution of a signal to be analyzed and a mother wavelet,based on scaling parameters such as a scale resolution and number ofscales. A scalogram may be generated based on the mother wavelet, and athreshold may be calculated for the scalogram. Scales meeting thethreshold may be candidate scales for determining respirationinformation. The respiration information may be determined from aselected scale of the candidate scales based on the waveletcharacteristic frequency corresponding to the selected scale.

An oximeter is a medical device that may determine the oxygen saturationof the blood. One common type of oximeter is a pulse oximeter, which mayindirectly measure the oxygen saturation of a patient's blood (asopposed to measuring oxygen saturation directly by analyzing a bloodsample taken from the patient). Pulse oximeters may be included inpatient monitoring systems that measure and display various blood flowcharacteristics including, but not limited to, the oxygen saturation ofhemoglobin in arterial blood. Such patient monitoring systems may alsomeasure and display additional physiological parameters, such as apatient's pulse rate.

An oximeter may include a light sensor that is placed at a site on apatient, typically a fingertip, toe, forehead or earlobe, or in the caseof a neonate, across a foot. The oximeter may use a light source to passlight through blood perfused tissue and photoelectrically sense theabsorption of the light in the tissue. In addition, locations that arenot typically understood to be optimal for pulse oximetry serve assuitable sensor locations for the monitoring processes described herein,including any location on the body that has a strong pulsatile arterialflow. For example, additional suitable sensor locations include, withoutlimitation, the neck to monitor carotid artery pulsatile flow, the wristto monitor radial artery pulsatile flow, the inside of a patient's thighto monitor femoral artery pulsatile flow, the ankle to monitor tibialartery pulsatile flow, and around or in front of the ear. Suitablesensors for these locations may include sensors for sensing absorbedlight based on detecting reflected light. In all suitable locations, forexample, the oximeter may measure the intensity of light that isreceived at the light sensor as a function of time. The oximeter mayalso include sensors at multiple locations. A signal representing lightintensity versus time or a mathematical manipulation of this signal(e.g., a scaled version thereof, a log taken thereof, a scaled versionof a log taken thereof, etc.) may be referred to as thephotoplethysmograph (PPG) signal. In addition, the term “PPG signal,” asused herein, may also refer to an absorption signal (i.e., representingthe amount of light absorbed by the tissue) or any suitable mathematicalmanipulation thereof. The light intensity or the amount of lightabsorbed may then be used to calculate any of a number of physiologicalparameters, including an amount of a blood constituent (e.g.,oxyhemoglobin) being measured as well as a pulse rate and when eachindividual pulse occurs.

In some applications, the light passed through the tissue is selected tobe of one or more wavelengths that are absorbed by the blood in anamount representative of the amount of the blood constituent present inthe blood. The amount of light passed through the tissue varies inaccordance with the changing amount of blood constituent in the tissueand the related light absorption. Red and infrared (IR) wavelengths maybe used because it has been observed that highly oxygenated blood willabsorb relatively less Red light and more IR light than blood with alower oxygen saturation. By comparing the intensities of two wavelengthsat different points in the pulse cycle, it is possible to estimate theblood oxygen saturation of hemoglobin in arterial blood.

When the measured blood parameter is the oxygen saturation ofhemoglobin, a convenient starting point assumes a saturation calculationbased at least in part on Lambert-Beer's law. The following notationwill be used herein:I(λ,t)=I _(o)(λ)exp(−(sβ _(o)(λ)+(1−s)β_(r)(λ))l(t)  (1)where:λ=wavelength;t=time;I=intensity of light detected;I₀=intensity of light transmitted;s=oxygen saturation;β₀,β_(r)=empirically derived absorption coefficients; andl(t)=a combination of concentration and path length from emitter todetector as a function of time.

The traditional approach measures light absorption at two wavelengths(e.g., Red and IR), and then calculates saturation by solving for the“ratio of ratios” as follows.

1. The natural logarithm of Eq. 1 is taken (“log” will be used torepresent the natural logarithm) for IR and Red to yieldlog I=log I _(o)−(sβ _(o)+(1−s)β_(r))l.  (2)2. Eq. 2 is then differentiated with respect to time to yield

$\begin{matrix}{\frac{{\mathbb{d}\log}\; I}{\mathbb{d}t} = {{- ( {{s\;\beta_{o}} + {( {1 - s} )\beta_{r}}} )}{\frac{\mathbb{d}l}{\mathbb{d}t}.}}} & (3)\end{matrix}$3. Eq. 3, evaluated at the Red wavelength λ_(R), is divided by Eq. 3evaluated at the IR wavelength λ_(IR) in accordance with

$\begin{matrix}{\frac{{\mathbb{d}\log}\;{{I( \lambda_{R} )}/{\mathbb{d}t}}}{{\mathbb{d}\log}\;{{I( \lambda_{IR} )}/{\mathbb{d}t}}} = {\frac{{s\;{\beta_{o}( \lambda_{R} )}} + {( {1 - s} ){\beta_{r}( \lambda_{R} )}}}{{s\;{\beta_{o}( \lambda_{IR} )}} + {( {1 - s} ){\beta_{r}( \lambda_{IR} )}}}.}} & (4)\end{matrix}$4. Solving for S yields

$\begin{matrix}{s = {\frac{{\frac{{\mathbb{d}\log}\;{I( \lambda_{IR} )}}{\mathbb{d}t}{\beta_{r}( \lambda_{R} )}} - {\frac{{\mathbb{d}\log}\;{I( \lambda_{R} )}}{\mathbb{d}t}{\beta_{r}( \lambda_{IR} )}}}{{\frac{{\mathbb{d}\log}\;{I( \lambda_{R} )}}{\mathbb{d}t}( {{\beta_{o}( \lambda_{IR} )} - {\beta_{r}( \lambda_{IR} )}} )} - {\frac{{\mathbb{d}\log}\;{I( \lambda_{IR} )}}{\mathbb{d}t}\begin{pmatrix}{{\beta_{o}( \lambda_{R} )} -} \\{\beta_{r}( \lambda_{R} )}\end{pmatrix}}}.}} & (5)\end{matrix}$5. Note that, in discrete time, the following approximation can be made:

$\begin{matrix}{\frac{{\mathbb{d}\log}\;{I( {\lambda,t} )}}{\mathbb{d}t} \simeq {{\log\;{I( {\lambda,t_{2}} )}} - {\log\;{{I( {\lambda,t_{1}} )}.}}}} & (6)\end{matrix}$6. Rewriting Eq. 6 by observing that log A−log B=log(A/B) yields

$\begin{matrix}{{\frac{{\mathbb{d}\log}\;{I( {\lambda,t} )}}{\mathbb{d}t} \simeq {{\log( \frac{I( {t_{2},\lambda} )}{I( {t_{1},\lambda} )} )}.}}\;} & (7)\end{matrix}$7. Thus, Eq. 4 can be expressed as

$\begin{matrix}{{{\frac{\frac{{\mathbb{d}\log}\;{I( \lambda_{IR} )}}{\mathbb{d}t}}{\frac{{\mathbb{d}\log}\;{I( \lambda_{IR} )}}{\mathbb{d}t}} \simeq \frac{\log( \frac{I( {t_{1},\lambda_{R}} )}{I( {t_{2},\lambda_{R}} )} )}{\log( \frac{I( {t_{1},\lambda_{IR}} )}{I( {t_{2},\lambda_{IR}} )} )}} = R},} & (8)\end{matrix}$where R represents the “ratio of ratios.”8. Solving Eq. 4 for s using the relationship of Eq. 5 yields

$\begin{matrix}{s = {\frac{{\beta_{r}( \lambda_{R} )} - {R\;{\beta_{r}( \lambda_{IR} )}}}{{R( {{\beta_{o}( \lambda_{IR} )} - {\beta_{r}( \lambda_{IR} )}} )} - {\beta_{o}( \lambda_{R} )} + {\beta_{r}( \lambda_{R} )}}.}} & (9)\end{matrix}$9. From Eq. 8, R can be calculated using two points (e.g., PPG maximumand minimum), or a family of points. One method applies a family ofpoints to a modified version of Eq. 8. Using the relationship

$\begin{matrix}{{\frac{{\mathbb{d}\log}\; I}{\mathbb{d}t} = \frac{{\mathbb{d}I}/{\mathbb{d}t}}{I}},} & (10)\end{matrix}$Eq. 8 becomes

$\begin{matrix}{{{\frac{\frac{{\mathbb{d}\log}\;{I( \lambda_{R} )}}{\mathbb{d}t}}{\frac{{\mathbb{d}\log}\;{I( \lambda_{IR} )}}{\mathbb{d}t}} \simeq \frac{\frac{{I( {t_{2},\lambda_{R}} )} - {I( {t_{1},\lambda_{R}} )}}{I( {t_{1},\lambda_{R}} )}}{\frac{{I( {t_{2},\lambda_{IR}} )} - {I( {t_{1},\lambda_{IR}} )}}{I( {t_{1},\lambda_{IR}} )}}} = {\frac{\lbrack {{I( {t_{2},\lambda_{R}} )} - {I( {t_{1},\lambda_{R}} )}} \rbrack{I( {t_{1},\lambda_{IR}} )}}{\lbrack {{I( {t_{2},\lambda_{IR}} )} - {I( {t_{1},\lambda_{IR}} )}} \rbrack{I( {t_{1},\lambda_{R}} )}} = R}},} & (11)\end{matrix}$which defines a cluster of points whose slope of y versus x will give Rwhenx=[I(t ₂,λ_(IR))−I(t ₁,λ_(IR))]I(t ₁,λ_(R)),  (12)andy=[I(t ₂,λ_(R))−I(t ₁,λ_(R))]I(t ₁,λ_(IR))  (13)Once R is determined or estimated, for example, using the techniquesdescribed above, the blood oxygen saturation can be determined orestimated using any suitable technique for relating a blood oxygensaturation value to R. For example, blood oxygen saturation can bedetermined from empirical data that may be indexed by values of R,and/or it may be determined from curve fitting and/or otherinterpolative techniques.

FIG. 1 is a perspective view of an embodiment of a patient monitoringsystem 10. System 10 may include sensor unit 12 and monitor 14. In someembodiments, sensor unit 12 may be part of an oximeter. Sensor unit 12may include an emitter 16 for emitting light at one or more wavelengthsinto a patient's tissue. A detector 18 may also be provided in sensorunit 12 for detecting the light originally from emitter 16 that emanatesfrom the patient's tissue after passing through the tissue. Any suitablephysical configuration of emitter 16 and detector 18 may be used. In anembodiment, sensor unit 12 may include multiple emitters and/ordetectors, which may be spaced apart. System 10 may also include one ormore additional sensor units (not shown) that may take the form of anyof the embodiments described herein with reference to sensor unit 12. Anadditional sensor unit may be the same type of sensor unit as sensorunit 12, or a different sensor unit type than sensor unit 12. Multiplesensor units may be capable of being positioned at two differentlocations on a subject's body; for example, a first sensor unit may bepositioned on a patient's forehead, while a second sensor unit may bepositioned at a patient's fingertip.

Sensor units may each detect any signal that carries information about apatient's physiological state, such as an electrocardiograph signal,arterial line measurements, or the pulsatile force exerted on the wallsof an artery using, for example, oscillometric methods with apiezoelectric transducer. According to another embodiment, system 10 mayinclude two or more sensors forming a sensor array in lieu of either orboth of the sensor units. Each of the sensors of a sensor array may be acomplementary metal oxide semiconductor (CMOS) sensor. Alternatively,each sensor of an array may be charged coupled device (CCD) sensor. Insome embodiments, a sensor array may be made up of a combination of CMOSand CCD sensors. The CCD sensor may comprise a photoactive region and atransmission region for receiving and transmitting data whereas the CMOSsensor may be made up of an integrated circuit having an array of pixelsensors. Each pixel may have a photodetector and an active amplifier. Itwill be understood that any type of sensor, including any type ofphysiological sensor, may be used in one or more sensor units inaccordance with the systems and techniques disclosed herein. It isunderstood that any number of sensors measuring any number ofphysiological signals may be used to determine physiological informationin accordance with the techniques described herein.

In some embodiments, emitter 16 and detector 18 may be on opposite sidesof a digit such as a finger or toe, in which case the light that isemanating from the tissue has passed completely through the digit. Insome embodiments, emitter 16 and detector 18 may be arranged so thatlight from emitter 16 penetrates the tissue and is reflected by thetissue into detector 18, such as in a sensor designed to obtain pulseoximetry data from a patient's forehead.

In some embodiments, sensor unit 12 may be connected to and draw itspower from monitor 14 as shown. In another embodiment, the sensor may bewirelessly connected to monitor 14 and include its own battery orsimilar power supply (not shown). Monitor 14 may be configured tocalculate physiological parameters (e.g., pulse rate, blood oxygensaturation, and respiration information) based at least in part on datarelating to light emission and detection received from one or moresensor units such as sensor unit 12 and an additional sensor (notshown). In some embodiments, the calculations may be performed on thesensor units or an intermediate device and the result of thecalculations may be passed to monitor 14. Further, monitor 14 mayinclude a display 20 configured to display the physiological parametersor other information about the system. In the embodiment shown, monitor14 may also include a speaker 22 to provide an audible sound that may beused in various other embodiments, such as for example, sounding anaudible alarm in the event that a patient's physiological parameters arenot within a predefined normal range. In some embodiments, the system 10includes a stand-alone monitor in communication with the monitor 14 viaa cable or a wireless network link.

In some embodiments, sensor unit 12 may be communicatively coupled tomonitor 14 via a cable 24. In some embodiments, a wireless transmissiondevice (not shown) or the like may be used instead of or in addition tocable 24. Monitor 14 may include a sensor interface configured toreceive physiological signals from sensor unit 12, provide signals andpower to sensor unit 12, or otherwise communicate with sensor unit 12.The sensor interface may include any suitable hardware, software, orboth, which may be allow communication between monitor 14 and sensorunit 12.

Patient monitoring system 10 may also include display monitor 26.Monitor 14 may be in communication with display monitor 26. Displaymonitor 26 may be any electronic device that is capable of communicatingwith monitor 14 and calculating and/or displaying physiologicalparameters, e.g., a general purpose computer, tablet computer, smartphone, or an application-specific device. Display monitor 26 may includea display 28 and user interface 30. Display 28 may include touch screenfunctionality to allow a user to interface with display monitor 26 bytouching display 28 and utilizing motions. User interface 30 may be anyinterface that allows a user to interact with display monitor 26, e.g.,a keyboard, one or more buttons, a camera, or a touchpad.

Monitor 14 and display monitor 26 may communicate utilizing any suitabletransmission medium, including wireless (e.g., WiFi, Bluetooth, etc.),wired (e.g., USB, Ethernet, etc.), or application-specific connections.In an exemplary embodiment, monitor 14 and display monitor 26 may beconnected via cable 32. Monitor 14 and display monitor 26 maycommunicate utilizing standard or proprietary communications protocols,such as the Standard Host Interface Protocol (SHIP) developed by theassignee. In addition, monitor 14, display monitor 26, or both may becoupled to a network to enable the sharing of information with serversor other workstations (not shown). Monitor 14, display monitor 26, orboth may be powered by a battery (not shown) or by a conventional powersource such as a wall outlet.

Monitor 14 may transmit calculated physiological parameters (e.g., pulserate, blood oxygen saturation, and respiration information) to displaymonitor 26. In some embodiments, monitor 14 may transmit a PPG signal,data representing a PPG signal, or both to display monitor 26, such thatsome or all calculated physiological parameters (e.g., pulse rate, bloodoxygen saturation, and respiration information) may be calculated atdisplay monitor 26. In an exemplary embodiment, monitor 14 may calculatepulse rate and blood oxygen saturation, while display monitor 26 maycalculate respiration information such as a respiration rate.

FIG. 2 is a block diagram of a patient monitoring system, such aspatient monitoring system 10 of FIG. 1, which may be coupled to apatient 40 in accordance with an embodiment. Certain illustrativecomponents of sensor unit 12 and monitor 14 are illustrated in FIG. 2.

Sensor unit 12 may include emitter 16, detector 18, and encoder 42. Inthe embodiment shown, emitter 16 may be configured to emit at least twowavelengths of light (e.g., Red and IR) into a patient's tissue 40.Hence, emitter 16 may include a Red light emitting light source such asRed light emitting diode (LED) 44 and an IR light emitting light sourcesuch as IR LED 46 for emitting light into the patient's tissue 40 at thewavelengths used to calculate the patient's physiological parameters. Insome embodiments, the Red wavelength may be between about 600 nm andabout 700 nm, and the IR wavelength may be between about 800 nm andabout 1000 nm. In embodiments where a sensor array is used in place of asingle sensor, each sensor may be configured to emit a singlewavelength. For example, a first sensor may emit only a Red light whilea second sensor may emit only an IR light. In a further example, thewavelengths of light used may be selected based on the specific locationof the sensor.

It will be understood that, as used herein, the term “light” may referto energy produced by radiation sources and may include one or more ofradio, microwave, millimeter wave, infrared, visible, ultraviolet, gammaray or X-ray electromagnetic radiation. As used herein, light may alsoinclude electromagnetic radiation having any wavelength within theradio, microwave, infrared, visible, ultraviolet, or X-ray spectra, andthat any suitable wavelength of electromagnetic radiation may beappropriate for use with the present techniques. Detector 18 may bechosen to be specifically sensitive to the chosen targeted energyspectrum of the emitter 16.

In some embodiments, detector 18 may be configured to detect theintensity of light at the Red and IR wavelengths. Alternatively, eachsensor in the array may be configured to detect an intensity of a singlewavelength. In operation, light may enter detector 18 after passingthrough the patient's tissue 40. Detector 18 may convert the intensityof the received light into an electrical signal. The light intensity isdirectly related to the absorbance and/or reflectance of light in thetissue 40. That is, when more light at a certain wavelength is absorbedor reflected, less light of that wavelength is received from the tissueby the detector 18. After converting the received light to an electricalsignal, detector 18 may send the signal to monitor 14, wherephysiological parameters may be calculated based on the absorption ofthe Red and IR wavelengths in the patient's tissue 40.

In some embodiments, encoder 42 may contain information about sensorunit 12, such as what type of sensor it is (e.g., whether the sensor isintended for placement on a forehead or digit) and the wavelengths oflight emitted by emitter 16. This information may be used by monitor 14to select appropriate algorithms, lookup tables and/or calibrationcoefficients stored in monitor 14 for calculating the patient'sphysiological parameters.

Encoder 42 may contain information specific to patient 40, such as, forexample, the patient's age, weight, and diagnosis. This informationabout a patient's characteristics may allow monitor 14 to determine, forexample, patient-specific threshold ranges in which the patient'sphysiological parameter measurements should fall and to enable ordisable additional physiological parameter algorithms. This informationmay also be used to select and provide coefficients for equations fromwhich measurements may be determined based at least in part on thesignal or signals received at sensor unit 12. For example, some pulseoximetry sensors rely on equations to relate an area under a portion ofa PPG signal corresponding to a physiological pulse to determine bloodpressure. These equations may contain coefficients that depend upon apatient's physiological characteristics as stored in encoder 42. Encoder42 may, for instance, be a coded resistor that stores valuescorresponding to the type of sensor unit 12 or the type of each sensorin the sensor array, the wavelengths of light emitted by emitter 16 oneach sensor of the sensor array, and/or the patient's characteristics.In some embodiments, encoder 42 may include a memory on which one ormore of the following information may be stored for communication tomonitor 14; the type of the sensor unit 12; the wavelengths of lightemitted by emitter 16; the particular wavelength each sensor in thesensor array is monitoring; a signal threshold for each sensor in thesensor array; any other suitable information; or any combinationthereof.

In some embodiments, signals from detector 18 and encoder 42 may betransmitted to monitor 14. In the embodiment shown, monitor 14 mayinclude a general-purpose microprocessor 48 connected to an internal bus50. Microprocessor 48 may be adapted to execute software, which mayinclude an operating system and one or more applications, as part ofperforming the functions described herein. Also connected to bus 50 maybe a read-only memory (ROM) 52, a random access memory (RAM) 54, userinputs 56, display 20, data output 84, and speaker 22.

RAM 54 and ROM 52 are illustrated by way of example, and not limitation.Any suitable computer-readable media may be used in the system for datastorage. Computer-readable media are capable of storing information thatcan be interpreted by microprocessor 48. This information may be data ormay take the form of computer-executable instructions, such as softwareapplications, that cause the microprocessor to perform certain functionsand/or computer-implemented methods. Depending on the embodiment, suchcomputer-readable media may include computer storage media andcommunication media. Computer storage media may include volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media may include, but is not limited to,RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store the desired informationand that can be accessed by components of the system.

In the embodiment shown, a time processing unit (TPU) 58 may providetiming control signals to light drive circuitry 60, which may controlwhen emitter 16 is illuminated and multiplexed timing for Red LED 44 andIR LED 46. TPU 58 may also control the gating-in of signals fromdetector 18 through amplifier 62 and switching circuit 64. These signalsare sampled at the proper time, depending upon which light source isilluminated. The received signal from detector 18 may be passed throughamplifier 66, low pass filter 68, and analog-to-digital converter 70.The digital data may then be stored in a queued serial module (QSM) 72(or buffer) for later downloading to RAM 54 as QSM 72 is filled. In someembodiments, there may be multiple separate parallel paths havingcomponents equivalent to amplifier 66, filter 68, and/or A/D converter70 for multiple light wavelengths or spectra received. Any suitablecombination of components (e.g., microprocessor 48, RAM 54, analog todigital converter 70, any other suitable component shown or not shown inFIG. 2) coupled by bus 50 or otherwise coupled (e.g., via an externalbus), may be referred to as “processing equipment.”

In some embodiments, microprocessor 48 may determine the patient'sphysiological parameters, such as SpO₂, pulse rate, and/or respirationinformation, using various algorithms and/or look-up tables based on thevalue of the received signals and/or data corresponding to the lightreceived by detector 18. Signals corresponding to information aboutpatient 40, and particularly about the intensity of light emanating froma patient's tissue over time, may be transmitted from encoder 42 todecoder 74. These signals may include, for example, encoded informationrelating to patient characteristics. Decoder 74 may translate thesesignals to enable the microprocessor to determine the thresholds basedat least in part on algorithms or look-up tables stored in ROM 52. Insome embodiments, user inputs 56 may be used to enter information,select one or more options, provide a response, input settings, anyother suitable inputting function, or any combination thereof. Userinputs 56 may be used to enter information about the patient, such asage, weight, height, diagnosis, medications, treatments, and so forth.In some embodiments, display 20 may exhibit a list of values, which maygenerally apply to the patient, such as, for example, age ranges ormedication families, which the user may select using user inputs 56.

Calibration device 80, which may be powered by monitor 14 via acommunicative coupling 82, a battery, or by a conventional power sourcesuch as a wall outlet, may include any suitable signal calibrationdevice. Calibration device 80 may be communicatively coupled to monitor14 via communicative coupling 82, and/or may communicate wirelessly (notshown). In some embodiments, calibration device 80 is completelyintegrated within monitor 14. In some embodiments, calibration device 80may include a manual input device (not shown) used by an operator tomanually input reference signal measurements obtained from some othersource (e.g., an external invasive or non-invasive physiologicalmeasurement system).

Data output 84 may provide for communications with other devices such asdisplay monitor 26 utilizing any suitable transmission medium, includingwireless (e.g., WiFi, Bluetooth, etc.), wired (e.g., USB, Ethernet,etc.), or application-specific connections. Data output 84 may receivemessages to be transmitted from microprocessor 48 via bus 50. Exemplarymessages to be sent in an embodiment described herein may include PPGsignals to be transmitted to display monitor module 26.

The optical signal attenuated by the tissue of patient 40 can bedegraded by noise, among other sources. One source of noise is ambientlight that reaches the light detector. Another source of noise iselectromagnetic coupling from other electronic instruments. Movement ofthe patient also introduces noise and affects the signal. For example,the contact between the detector and the skin, or the emitter and theskin, can be temporarily disrupted when movement causes either to moveaway from the skin. Also, because blood is a fluid, it respondsdifferently than the surrounding tissue to inertial effects, which mayresult in momentary changes in volume at the point to which the oximeterprobe is attached.

Noise (e.g., from patient movement) can degrade a sensor signal reliedupon by a care provider, without the care provider's awareness. This isespecially true if the monitoring of the patient is remote, the motionis too small to be observed, or the care provider is watching theinstrument or other parts of the patient, and not the sensor site.Processing sensor signals (e.g., PPG signals) may involve operationsthat reduce the amount of noise present in the signals, control theamount of noise present in the signal, or otherwise identify noisecomponents in order to prevent them from affecting measurements ofphysiological parameters derived from the sensor signals.

FIG. 3 is an illustrative processing system 300 in accordance with anembodiment that may implement the signal processing techniques describedherein. In some embodiments, processing system 300 may be included in apatient monitoring system (e.g., patient monitoring system 10 of FIGS.1-2). Processing system 300 may include input signal 310, pre-processor312, processor 314, post-processor 316, and output 318. Pre-processor312, processor 314, and post-processor 316 may be any suitable software,firmware, hardware, or combination thereof for calculating physiologicalparameters such as respiration information based on input signal 310.For example, pre-processor 312, processor 314, and post-processor 316may include one or more hardware processors (e.g., integrated circuits),one or more software modules, computer-readable media such as memory,firmware, or any combination thereof. Pre-processor 312, processor 314,and post-processor 316 may, for example, be a computer or may be one ormore chips (i.e., integrated circuits). Pre-processor 312, processor314, and post-processor 316 may, for example, include an assembly ofanalog electronic components.

In some embodiments, processing system 300 may be included in monitor 14and/or display monitor 26 of a patient monitoring system (e.g., patientmonitoring system 10 of FIGS. 1-2). In the illustrated embodiment, inputsignal 310 may be a PPG signal. Input signal 310 may be a PPG signalthat was sampled and generated at monitor 14, for example at 76 Hz.Input signal 310, pre-processor 312, processor 314, and post-processor316 may reside entirely within a single device (e.g., monitor 14 ordisplay monitor 26) or may reside in multiple devices (e.g., monitor 14and display monitor 26).

Input signal 310 may be coupled to pre-processor 312. In someembodiments, input signal 310 may include PPG signals corresponding toone or more light frequencies, such as a Red PPG signal and an IR PPGsignal. In some embodiments, the signal may include signals measured atone or more sites on a patient's body, for example, a patient's finger,toe, ear, arm, or any other body site. In some embodiments, signal 310may include multiple types of signals (e.g., one or more of an ECGsignal, an EEG signal, an acoustic signal, an optical signal, a signalrepresenting a blood pressure, and a signal representing a heart rate).The signal may be any suitable biosignal or signals, such as, forexample, electrocardiogram, electroencephalogram, electrogastrogram,electromyogram, heart rate signals, pathological sounds, ultrasound, orany other suitable biosignal. The systems and techniques describedherein are also applicable to any dynamic signals, non-destructivetesting signals, condition monitoring signals, fluid signals,geophysical signals, astronomical signals, electrical signals, financialsignals including financial indices, sound and speech signals, chemicalsignals, meteorological signals including climate signals, any othersuitable signal, and/or any combination thereof.

Pre-processor 312 may be implemented by any suitable combination ofhardware and software. In an embodiment, pre-processor 312 may be anysuitable signal processing device and the signal received from inputsignal 310 may include one or more PPG signals. An exemplary receivedPPG signal may be received in a streaming fashion, or may be received ona periodic basis as a sampling window, e.g., every 5 seconds. Thereceived signal may include the PPG signal as well as other informationrelated to the PPG signal, e.g., a pulse found indicator, the mean pulserate from the PPG signal, the most recent pulse rate, an indicator forthe most recent invalid sample, and an indicator of the last artifactfor the PPG signal. It will be understood that input signal 310 mayinclude any suitable signal source, signal generating data, signalgenerating equipment, or any combination thereof to be provided topre-processor 312. The signal received at input signal 310 may be asingle signal, or may be multiple signals transmitted over a singlepathway or multiple pathways.

Pre-processor 312 may apply one or more signal processing operations toinput signal 310. For example, pre-processor 312 may apply apre-determined set of processing operations to input signal 310 toproduce a signal that may be appropriately analyzed and interpreted byprocessor 314, post-processor 316, or both. Pre-processor 312 mayperform any necessary operations to provide a signal that may be used asan input for processor 314 and post-processor 316 to determinephysiological information such as respiration information. Examplesinclude reshaping the signal for transmission, multiplexing the signal,modulating the signal onto carrier signals, compressing the signal,encoding the signal, filtering the signal, low-pass filtering, band-passfiltering, signal interpolation, downsampling of a signal, attenuatingthe signal, adaptive filtering, closed-loop filtering, any othersuitable filtering, and/or any combination thereof.

Other signal processing operations may be performed by pre-processor 312for each pulse and may be related to producing morphology metricssuitable as inputs to determine physiological information. Pre-processor312 may perform calculations based on an analysis window of a series ofrecently received PPG signal sampling windows, e.g., a 45-secondanalysis window may correspond to the 9 most recent 5-second samplingwindows. The physiological information may be respiration information,which may include any information relating to respiration, e.g.,respiration rate, change in respiration rate, breathing intensity, etc.Because respiration has an impact on pulse characteristics, it may bepossible to determine respiration information from a PPG signal.Morphology metrics may be parameters that may be calculated from the PPGsignal that provide information related to respiration. Examples includea down metric for a pulse, kurtosis for a pulse, the delta of the secondderivative between consecutive pulses, the up metric for a pulse, skew,b/a ratio, c/a ratio, peak amplitude of a pulse, center of gravity of apulse, or area of a pulse, as described in more detail herein. Otherinformation that may be determined by pre-processor 312 may include thepulse rate, the variability of the period of the PPG signal, thevariability of the amplitude of the PPG signal, and an age measurementindicative of the age of the useful portion of the analyzed PPG signal.

In some embodiments, pre-processor 312 may be coupled to processor 314and post-processor 316. Processor 314 and post-processor 316 may beimplemented by any suitable combination of hardware and software.Processor 314 may receive physiological information and calculatedparameters from pre-processor 312. For example, processor may receivemorphology metrics for use in calculating morphology metric signals thatmay be used to determine respiration information, as well as pulse rateand an age for the morphology metric signals. For example, processor 314may receive samples representing a number of morphology metric values,such as down metric calculations, kurtosis metric calculations, anddelta of the second derivative (DSD) metric calculations frompre-processor 312. Processor 314 may utilize the received morphologymetric values to calculate morphology metric signals and then tocalculate respiration information signals and values from the morphologymetric signals. Processor 314 may be coupled to post-processor 316 andmay communicate respiration information to post-processor 316. Processor314 may also provide other information to post-processor 316 such as thesignal age related to the signal used to calculate the respirationinformation, and a time ratio representative of the useful portion ofthe respiration information signal. Pre-processor 312 may also provideinformation to post-processor 316 such as period variability, amplitudevariability, and pulse rate information. Post-processor 316 may utilizethe received information to calculate an output respiration information,as well as other information such as the age of the respirationinformation and status information relating to the respirationinformation output, e.g., whether a valid output respiration informationvalue is currently available. Post-processor 316 may provide the outputinformation to output 318.

Output 318 may be any suitable output device such as one or more medicaldevices (e.g., a medical monitor that displays various physiologicalparameters, a medical alarm, or any other suitable medical device thateither displays physiological parameters or uses the output ofpost-processor 316 as an input), one or more display devices (e.g.,monitor, PDA, mobile phone, any other suitable display device, or anycombination thereof), one or more audio devices, one or more memorydevices (e.g., hard disk drive, flash memory, RAM, optical disk, anyother suitable memory device, or any combination thereof), one or moreprinting devices, any other suitable output device, or any combinationthereof.

In some embodiments, all or some of pre-processor 312, processor 314,and/or post-processor 316 may be referred to collectively as processingequipment. For example, processing equipment may be configured toamplify, filter, sample and digitize an input signal 310 and calculatephysiological information from the signal.

Pre-processor 312, processor 314, and post-processor 316 may be coupledto one or more memory devices (not shown) or incorporate one or morememory devices such as any suitable volatile memory device (e.g., RAM,registers, etc.), non-volatile memory device (e.g., ROM, EPROM, magneticstorage device, optical storage device, flash memory, etc.), or both.The memory may be used by pre-processor 312, processor 314, andpost-processor 316 to, for example, store data relating to input PPGsignals, morphology metrics, respiration information, or otherinformation corresponding to physiological monitoring.

It will be understood that system 300 may be incorporated into system 10(FIGS. 1 and 2) in which, for example, input signal 310 may be generatedby sensor unit 12 (FIGS. 1 and 2) and monitor 14 (FIGS. 1 and 2).Pre-processor 312, processor 314, and post-processor 316 may each belocated in one of monitor 14 or display monitor 26 (or other devices),and may be split among multiple devices such as monitor 14 or displaymonitor 26. In some embodiments, portions of system 300 may beconfigured to be portable. For example, all or part of system 300 may beembedded in a small, compact object carried with or attached to thepatient (e.g., a watch, other piece of jewelry, or a smart phone). Insome embodiments, a wireless transceiver (not shown) may also beincluded in system 300 to enable wireless communication with othercomponents of system 10 (FIGS. 1 and 2). As such, system 10 (FIGS. 1 and2) may be part of a fully portable and continuous patient monitoringsolution. In some embodiments, a wireless transceiver (not shown) mayalso be included in system 300 to enable wireless communication withother components of system 10. For example, communications between oneor more of pre-processor 312, processor 314, and post-processor 316 maybe over BLUETOOTH, 802.11, WiFi, WiMax, cable, satellite, Infrared, orany other suitable transmission scheme. In some embodiments, a wirelesstransmission scheme may be used between any communicating components ofsystem 300.

Pre-processor 312 may determine the locations of pulses within aperiodic signal (e.g., a PPG signal) using a pulse detection technique.For ease of illustration, the following pulse detection techniques willbe described as performed by pre-processor 312, but any suitableprocessing device may be used to implement any of the techniquesdescribed herein.

An illustrative PPG signal 400 is depicted in FIG. 4. Pre-processor 312may receive PPG signal 400 from input signal 310, and may identifyreference points such as local minimum point 410, local maximum point412, local minimum point 420, local maximum point 422, and local minimumpoint 430 in the PPG signal 400. Processor 312 may pair each localminimum point with an adjacent maximum point. For example, processor 312may pair points 410 and 412 to identify one segment, points 412 and 420to identify a second segment, points 420 and 422 to identify a thirdsegment and points 422 and 430 to identify a fourth segment. The slopeof each segment may be measured to determine whether the segmentcorresponds to an upstroke portion of the pulse (e.g., a positive slope)or a downstroke portion of the pulse (e.g., a negative slope) portion ofthe pulse. A pulse may be defined as a combination of at least oneupstroke and one downstroke. For example, the segment identified bypoints 410 and 412 and the segment identified by points 412 and 430 maydefine a pulse. Any suitable points (e.g., maxima, minima, zeros) orfeatures (e.g., pulse waves, notches, upstrokes) of a physiologicalsignal may be identified by processor 312 as reference points.

PPG signal 400 may include a dichrotic notch 450 or other notches (notshown) in different sections of the pulse (e.g., at the beginning(referred to as an ankle notch), in the middle (referred to as adichrotic notch), or near the top (referred to as a shoulder notch)).Notches (e.g., dichrotic notches) may refer to secondary turning pointsof pulse waves as well as inflection points of pulse waves.Pre-processor 312 may identify notches and either utilize or ignore themwhen detecting the pulse locations. In some embodiments, pre-processor312 may compute the second derivative of the PPG signal to find thelocal minima and maxima points and may use this information to determinea location of, for example, a dichrotic notch. Additionally,pre-processor 312 may interpolate between points in a signal or betweenpoints in a processed signal using any interpolation technique (e.g.,zero-order hold, linear interpolation, and/or higher-order interpolationtechniques). Some pulse detection techniques that may be performed bypre-processor 312 are described in more detail in co-pending, commonlyassigned U.S. patent application Ser. No. 12/242,908, filed Sep. 30,2008 and entitled “SYSTEMS AND METHODS FOR DETECTING PULSES IN A PPGSIGNAL,” which is incorporated by reference herein in its entirety.

In some embodiments, reference points may be received or otherwisedetermined from any other suitable pulse detecting technique. Forexample, pulse beep flags generated by a pulse oximeter, which mayindicate when the pulse oximeter is to emit an audible beep, may bereceived by processor 314, pre-processor 312, post-processor 316, or anycombination thereof for processing in accordance with the presentdisclosure. The pulse beep flags may be used as reference pointsindicative of the occurrence of a pulses in temporally correspondingplaces in the associated PPG signal.

An illustrative PPG signal 500 is depicted in FIG. 5. FIG. 6 shows theillustrative signal of FIG. 5 including further analysis. Processor 314may receive PPG signal 500, and may locate successive reference points502 and 512 corresponding to respective, successive pulse waves. In someembodiments, reference points may be, for example, maxima in the firstderivative of PPG signal 500, as illustrated in FIG. 5 by referencepoints 502 and 512. Interval 510, between reference points 502 and 512,may correspond to the duration of a pulse wave. For example, the inverseof interval 510 may be proportional to a pulse rate (e.g., in units ofbeats per minute (BPM) or Hz).

In some embodiments, pre-processor 312 (or any other suitable processor)may locate a fiducial point at point 514 for further calculations basedon a reference point. For example, using point 502 as a reference point,pre-processor 312 may locate point 514 by translating a particular time(or corresponding number of samples) from point 502 in a particulardirection along PPG signal 500, as shown by time interval 522 of FIG. 6.Another exemplary reference point may be a maximum point 504 withininterval 510. In some embodiments, processor 312 may use point 504 as areference point to locate a further fiducial point at point 514, asshown in FIG. 6. For example, using point 504 as a reference point,processor 312 may locate point 514 by translating a particular time (orcorresponding number of samples) from point 504 in a particulardirection along PPG signal 500, as shown by time interval 520 of FIG. 6.Point 514 may be a fiducial point, and may be used in furtherphysiological calculations. The number of samples defining a fiducialpoint from a reference point (or any other suitable point derived fromthe PPG signal or from the reference point) may be determined accordingto, for example, empirical analysis. In some embodiments, the fiducialpoint may be the same as a reference point (i.e., once a reference pointis determined, no additional processing is necessary to identify acorresponding fiducial point).

Respiratory activities may cause particular changes in the morphology ofa PPG signal throughout a respiratory cycle, including, for example, ona pulse by pulse basis. In some circumstances, these changes inmorphology may be in addition to morphological change due to changes instroke volume, pulse rate, blood pressure, any other suitablephysiological parameters, or any combination thereof. Respiratorymodulations may include baseline modulations, amplitude modulations,frequency modulations, respiratory sinus arrhythmia, any other suitablemodulations, or any combination thereof. Respiratory modulations mayexhibit different phases, amplitudes, or both, within a PPG signal andmay contribute to complex behavior (e.g., changes) of the PPG signal.Morphology metrics may be calculated on any portion of a PPG signal, butin one exemplary embodiment each consecutive set of fiducial points maydefine a relevant portion of the PPG signal for calculating a morphologymetric, and may be referred to herein as a fiducial-defined portion.

In some embodiments, a set of fiducial points on a sampled physiologicalsignal or signal derived thereof (e.g., a derivative of a signal, asmoothed signal, a filtered signal, an amplified signal, or otherprocessed signal) may be further processed (e.g., by pre-processor 312).In some embodiments, a set of fiducial points, corresponding to a subsetof points on the sampled signal, may be used to create a fiducial signalor as a reference to calculate morphology metric values. For example, asingle point on each pulse wave may be used to create the fiducialsignal or as a basis for calculating a morphology metric valueassociated with a fiducial defined portion. The fiducial signal may befurther analyzed to, for example, calculate physiological parameters(e.g., respiration information), signal quality metrics, any othersuitable values, or any combination thereof, e.g., by processor 314 andpost-processor 316.

In an illustrative example, in some embodiments, a set of fiducialpoints on a PPG signal (e.g., a collection of points of successive pulsewaves each similar to point 514 of FIG. 6) may be outputted as afiducial signal. In another illustrative example, a set of fiducialpoints on a PPG signals may be utilized as a basis to determine one ormore sets of morphology metric values. The resulting fiducial signal ormorphology metric values may be further processed to calculaterespiration rate, respiratory modulation metrics, any other suitablerespiration information, any other suitable physiological parameters,any other suitable metrics, or any combination thereof.

The selection of fiducial points may influence processing of thefiducial signal or morphology metric values. In some embodiments,selection of fiducial points may be optimized to enhance the performanceof an analysis applied to the fiducial signal or morphology metricvalues. For example, a PPG signal may be pre-processed to emphasize keymorphological changes, which may aid in the extraction of respiratoryinformation using further processing (e.g., using an autocorrelation orwavelet transform). Pre-processing may include generating derivedsignals such as, for example, derivative, integral, or moving averagedsignals, which may be more amenable to particular analysis in somecircumstances. Pre-processing may also include determining one or morereference points, determining one or more fiducial points, or both.

FIG. 7 shows illustrative graphs 700, 720, and 740 depicting determiningfiducial points from a PPG signal. Each of graphs 700, 720, and 740include illustrative time series 710 shown by a solid line, and a set ofpoints shown by a set of circles. The abscissa of graphs 700, 720, and740 are in units of time, while the ordinate of graphs 700, 720, and 740are in units of signal amplitude.

Time series 710 shows a series of pulse waves of an illustrative PPGsignal. The set of points 702 represented by circles in graph 700correspond to the peak in the first derivative of each pulse wave. Insome embodiments, the set of points 702 may be used as reference points,fiducial points, or both. In the illustrated embodiment, the set ofpoints 702 represents a set of reference points. Although points 702correspond to the peak of the first derivative of each pulse wave, otherreference points may utilized, such as the maximum amplitude of eachpulse wave.

The set of points 722 represented by circles in graph 720 correspond topoints 16 samples (i.e., about 210 milliseconds at a sampling rate ofabout 76 Hertz) to the right of the peak in the first derivative of eachpulse wave (i.e., points 702). The set of points 722 may be a set offiducial points, selected using the peaks in the first derivative ofeach pulse wave as reference points and locating a set of respectivepoints spaced from the reference points by a particular time interval.Time series 730, including the set of points 722, represents a “fiducialsignal” derived from time series 720. Fiducial points 722 may also beutilized to determine other parameters, such as determining one or moremorphology metrics as described herein.

The set of points 742 represented by circles in graph 740 correspond topoints 22 samples to the right of the peak in the first derivative ofeach pulse wave. The set of points 742 may be a set of fiducial points,selected using the peaks in the first derivative of each pulse wave asreference points and locating a set of respective points spaced from thereference points by a particular time interval. Although not depictedherein, the fiducial points defined by the set of points 742 may beutilized to determine a fiducial signal, determine morphology metrics,or other parameters as described herein. Fiducial points may also belocated at other locations relative to the reference points.

In some embodiments, processor 314 or post-processor 316 may utilizefiducial points 722 or 742 as a basis for determining morphology metricsas described herein to determine physiological information. Time series730 may also be processed to determine physiological information. Forexample, processor 314 or post-processor 316 may determine respirationinformation such as a respiration rate from morphology metrics based onfiducial points 722 or 742, or from time series 730. For example,respiratory activity may be observed by the oscillatory character (at alonger time scale than that of the pulse rate shown by time series 710)of time series 730. Respiration information (e.g., respiration rate,respiration modulation shape) may be calculated by processor 314 orpost-processor 316 using any suitable mathematical processing techniques(e.g., using wavelet transforms, spectral transforms, curve-fitting). Insome embodiments, a particular set of points (e.g., the set of points722 located about 210 milliseconds to the right of the peak in firstderivative) may allow processor 314, post-processor 316, or both tocalculate physiological information with relatively more accuracy,relatively less computational requirements, relatively more consistency,any other suitable relative computational advantage, or any combinationthereof.

FIG. 8 shows a PPG signal from which reference points and fiducialpoints may be derived as illustrated in graphs 800. Each graph includesa time series of an illustrative PPG signal shown by a solid line, afirst set of points shown by triangles, and a second set of points shownby circles. The abscissa of each graph is in units of time, while theordinate of each graph is in units of signal amplitude.

Time series 810 includes a series of pulse waves of an illustrative PPGsignal. The set of points 804 represented by triangles in graph 800correspond to the peak in the first derivative of each pulse wave.Although points 804 correspond to the peak of the first derivative ofeach pulse wave, other reference points may utilized, such as themaximum amplitude of each pulse wave. The set of points 802 representedby circles correspond to illustrative reference points (e.g., referencepoints indicating “pulse found”). Any suitable technique may be used toidentify pulses in a PPG, including any known techniques or any futuretechniques currently not known.

The set of points 824 represented by triangles in graph 820 correspondto points located 14 samples (about 184 ms at a 76 Hz sampling rate) tothe right of (i.e., after) the reference points of each pulse wave(i.e., peak in the first derivative points 804). The set of points 824are roughly coincident with the set of points 802. In some embodiments,the set of points 824 may be used as a set of fiducial points, ratherthan locating the set of points 802. For example, pre-processor 312 mayuse the set of points 824 to indicate where a pulse has been detected.The use of the set of points 824 may allow processing system 300 tocalculate physiological information with relatively more accuracy,relatively less computational requirements, relatively more consistency,any other suitable relative computational advantage, or any combinationthereof. In some circumstances, the set of points 824 may be preferredto the set of points 802 because the set of points 824 are derived fromthe morphology of the signal and may be in phase with the morphology ofthe signal. In some circumstances, the set of points 802 may bedependent on the manner that location is determined, and the use of theset of points 824 may provide an improvement.

The set of points 844 represented by triangles in graph 840 correspondsto points located 22 samples to the right of the reference points ofeach pulse wave (i.e., peak in the first derivative points 804). The setof points represented by circles 802 corresponds to the same referencepoints of graph 800. In some embodiments, processor 312 may determinethat the set of points 844 is not to be used as a set of fiducial pointsbecause, for example, the set of points 844 is not substantiallycoincident with the set of points 802.

FIG. 9 is flow diagram 900 showing illustrative steps for determiningphysiological information, in accordance with the present disclosure.

Step 902 may include pre-processor 312 determining one or more referencepoints of a physiological signal. Determining the one or more referencepoints of the physiological signal may include receiving thephysiological signal from a sensor, conditioning the physiologicalsignal (e.g., amplifying, filtering, sampling, digitizing), performingcalculations on the physiological signal or conditioned signal thereof,selecting a time interval (or a corresponding number of samples) of thephysiological signal or conditioned signal thereof to analyze, any othersuitable processing, or any combination thereof. In some embodiments, asingle reference point on a signal may be determined by pre-processor312 such as, for example, an absolute minimum or maximum of a signal. Insome embodiments, a set of reference points may be determined bypre-processor 312. For example, pre-processor 312 may be configured toprocess a PPG signal that includes a set of pulse waves, and determine areference point for each pulse wave. Reference points on a signal mayinclude minimums on the signal, maximums on the signal, zeros on thesignal, minimums on a derivative (of any suitable order) of the signal,maximums on a derivative (of any suitable order) of the signal, zeros ona derivative (of any suitable order) of the signal, any other suitablepoints on a signal or other signal derived thereof, or any combinationthereof. For example, pre-processor 312 may determine two referencepoints, which may be maxima in the first derivative of two successivepulse waves of a PPG signal. In a further example, pre-processor 312 maydetermine a reference point, which may be a maximum or a minimum of thefirst derivative of a single pulse wave of a PPG signal. In a furtherexample, pre-processor 312 may determine a reference point, which may bea maximum of a pulse wave of a PPG signal.

Step 904 may include pre-processor 312 determining one or more fiducialpoints on the physiological signal of step 902 using the one or morereference points of step 902. Determining the one or more fiducialpoints of the physiological signal may include using a time intervalrelative to the one or more particular reference points of step 902,using a number of samples relative to the one or more particularreference points of step 902, any other suitable approaches ofdetermining a location of one or more fiducial points on a signal, orany combination thereof. For example, determining a fiducial point mayinclude locating a point on the physiological signal at a particulartime interval or number of samples from a reference point.

Step 906 may include pre-processor 312 determining physiologicalinformation based at least in part on the determined one or morefiducial points of step 904. Determining physiological information mayinclude performing calculations directly on the one or more fiducialpoints, generating morphology metric values and signals based on thefiducial points, generating a fiducial signal based on the one or morefiducial points, performing calculations on the morphology metric valuesor fiducial signal, calculating one or more physiological parameters(e.g., pulse rate, respiration rate, SpO₂, blood pressure), any othersuitable processing to determine physiological information, or anycombination thereof. In some embodiments, a pre-constructed program maybe executed by processing system 300 to determine physiologicalinformation from one or more fiducial points. For example, a programexecuted by pre-processor 312 may take as inputs a set of fiducialpoints and calculate one or more sets of morphology metric values.Pre-processor 312 may derive morphology metric signals from themorphology metric values and processor 314 or post-processor 316 maydetermine respiration information such as respiration rate from themorphology metric values, e.g., by applying a continuous wavelettransform on a combined autocorrelation of the morphology metricsignals. The transform may yield a dominant component (e.g., aparticular scale in the wavelet domain), which may indicate a rate of anoscillatory physiological activity, such as a respiration rate. In afurther example, a program executed by pre-processor 312 may take asinputs a set of fiducial points represented by a new time series. Theprogram may determine one or more fiducial points of the new time seriessuch as, for example, the peak to peak time interval of the fiducialsignal, which may yield physiological information such as respirationrate. Processing system 300 may determine physiological information byperforming any suitable calculation, executing any suitable analysis orprogram, performing any suitable database search, any other suitablesteps, or any combination thereof.

In some embodiments, determining the one or more fiducial points of thephysiological signal may include pre-processor 312 accessing fiducialinformation, as shown by step 908. Accessing fiducial information mayinclude recalling a mathematical expression, accessing a database (e.g.,a look up table), accessing memory, using a pre-set approach fordetermining fiducial points, receiving a user input selecting anapproach for determining fiducial points, any other suitable accessingof stored information, any other suitable accessing of user inputtedinformation, or any combination thereof. For example, step 908 mayinclude using a physiological parameter value in a lookup table todetermine a fiducial point type, a fiducial point location, or othersuitable fiducial information. In a further example, step 908 mayinclude inputting a physiological parameter value such as a pulse rateinto a mathematical formula, which may output a fiducial point locationrelative to a reference point (e.g., a time interval or number ofsample), or other suitable fiducial information.

FIG. 10 is flow diagram 1000 showing illustrative steps for determiningrespiratory information, in accordance with the present disclosure.

Step 1002 may include pre-processor 312 locating two reference points ofa PPG signal. Locating the two reference points of the PPG signal mayinclude determining a minimum, maximum, zero, any other suitable pointson a signal or other signal derived thereof (e.g., a derivative of anysuitable order), or any combination thereof. For example, the tworeference points may be two successive maxima in the first derivative ofthe PPG signal. In a further example, the two reference points may be asuccessive maximum and a minimum of the first derivative of the PPGsignal.

Step 1004 may include pre-processor 312 locating a maximum on the PPGsignal between the two located reference points of step 1002. In someembodiments, pre-processor 312 may locate a single maximum signal valuebetween the two reference points. For example, the two reference pointsmay be successive maxima in the first derivative of the PPG signal, andthe maximum on the PPG signal may correspond to a peak of a portion ofthe PPG signal (e.g., as shown by point 504 of FIG. 5). In a furtherexample, the two reference points may be a maximum and a minimum of thefirst derivative of the PPG signal (e.g., corresponding to a respectiveupstroke and downstroke of a pulse wave), and the maximum may correspondto a peak of a portion of the PPG signal.

Step 1006 may include pre-processor 312 selecting a fiducial point ofthe PPG signal. In some embodiments, pre-processor 312 may select afiducial point located a particular time interval (or correspondingnumber of samples) from the located maximum of step 1004. For example,pre-processor 312 may select a fiducial point located about 210milliseconds (approximately 16 samples at a sampling rate of about 76Hertz) to the right of the located maximum of step 1004. In someembodiments, the particular time interval may depend on physiologicalinformation (e.g., the patient's pulse rate, respiration rate,physiological history), and need not be a fixed interval. For example,the particular time interval may be the period corresponding to 10% ofthe average or instantaneous pulse period of the patient (e.g., 100milliseconds for a pulse period of 1 second). In a further example, theparticular time interval may be based at least in part on previouslycalculated respiration information such as respiration rate (e.g., usinga look up table of various calculated respiration rates to find anoptimum time interval). In some embodiments, pre-processor 312 mayselect multiple fiducial points. In some embodiments, pre-processor 312may perform steps 1002 and 1004 repeatedly, locating a set of maximabetween a corresponding set of pairs of reference points. A set ofcorresponding fiducial points may then be selected. For example, afiducial point may be selected for each reference point of a PPG signal,resulting in a set of fiducial points.

In some embodiments, selecting the fiducial point of step 1006 mayinclude pre-processor 312 accessing fiducial information, as shown bystep 1010. Accessing fiducial information may include recalling amathematical expression, accessing a database, accessing memory, using apre-set approach for determining fiducial points, receiving a user inputselecting an approach for determining fiducial points, any othersuitable accessing of stored information, any other suitable accessingof user inputted information, or any combination thereof.

Step 1008 may include processing system 300 determining respiratoryinformation based at least in part on the selected fiducial point ofstep 1006. Respiratory information may include respiration rate,respiratory modulation shape, any other suitable information, or anycombination thereof. Processor 314 or post-processor 316 may determinerespiratory information by calculating the peak to peak time interval ofa set of selected fiducial points, generating morphology metric signalsbased on fiducial points, performing an autocorrelation of the set ofselected fiducial points or morphology metric signals and determiningone or more peaks, performing a transform (e.g., a wavelet transform, aFourier transform) on the set of selected fiducial points, morphologymetric signals, or autocorrelation sequences, performing any othersuitable calculation, or any combination thereof.

FIG. 11 is flow diagram 1100 showing illustrative steps for generating afiducial signal from a physiological signal, in accordance with thepresent disclosure.

Step 1102 may include pre-processor 312 locating two reference points ofa PPG signal. Locating the two reference points of the PPG signal mayinclude determining a minimum, maximum, zero, any other suitable pointson a signal or other signal derived thereof (e.g., a derivative of anysuitable order), or any combination thereof. For example, the tworeference points may be two successive maxima in the first derivative ofthe PPG signal. In a further example, the two reference points may be asuccessive maximum and a minimum of the first derivative of the PPGsignal.

Step 1104 may include pre-processor 312 locating a maximum on the PPGsignal between the two located reference points of step 1102. In someembodiments, pre-processor 312 may locate a single maximum between thetwo reference points. For example, the two reference points may besuccessive maxima in the first derivative of the PPG signal, and themaximum on the PPG signal may correspond to peak of a portion of the PPGsignal (e.g., as shown by point 504 of FIG. 5). In a further example,the two reference points may be a successive maximum and a minimum ofthe first derivative of the PPG signal, and the maximum on the PPGsignal may correspond to a peak of a portion of the PPG signal.

Step 1106 may include pre-processor 312 selecting a fiducial point ofthe PPG signal. In some embodiments, pre-processor 312 may select afiducial point located a particular time interval (or correspondingnumber of samples) from the located maximum of step 1104. For example,pre-processor 312 may select a fiducial point located about 210milliseconds (approximately 16 samples at a sampling rate of about 76Hertz) to the right of the located maximum of step 1104. In someembodiments, the particular time interval may depend on physiologicalinformation, and need not be a fixed interval. For example, theparticular time interval may be the period corresponding to 10% of theaverage or instantaneous heart rate of the patient. In a furtherexample, the particular time interval may be based at least in part onpreviously calculated respiration information. In some embodiments,pre-processor 312 may select a set of fiducial points. In someembodiments, pre-processor 312 may perform steps 1102 and 1104repeatedly, locating a set of maxima between a corresponding set ofpairs of reference points. A set of corresponding fiducial points maythen be selected. For example, a fiducial point may be selected on eachpulse wave of a set of pulse waves of a PPG signal, resulting in a setof fiducial points.

In some embodiments, selecting the fiducial point of step 1106 mayinclude pre-processor 312 accessing fiducial information, as shown bystep 1110. Accessing fiducial information may include recalling amathematical expression, accessing a database, accessing memory, using apre-set approach for determining fiducial points, receiving a user inputselecting an approach for determining fiducial points, any othersuitable accessing of stored information, any other suitable accessingof user inputted information, or any combination thereof.

Step 1108 may include processing system 300 generating a fiducial signalbased at least in part on the selected fiducial point of step 1106. Insome embodiments the fiducial signal includes a set of selected fiducialpoints (e.g., as shown by time series 730 of FIG. 7). At step 1108,processing system 300 may average, filter, output (e.g., via acommunications interface), store in memory, or otherwise process, thefiducial signal. In some embodiments, physiological calculation may beperformed using the fiducial signal of step 1108.

FIG. 12 is flow diagram 1200 showing illustrative steps for evaluating aset of fiducial signals, in accordance with the present disclosure.

Step 1202 may include pre-processor 312 receiving a physiologicalsignal. In some embodiments, the physiological signal may be received bypre-processor 312 as input signal 310 from one or more physiologicalsensors (e.g., PPG sensors). In some embodiments, the physiologicalsignal may have been stored in memory (e.g., ROM 52 or RAM 54 of FIG.2), and may be recalled by pre-processor 312 from the memory. Step 1202may include conditioning the physiological signal such as, for example,amplifying, filtering, baseline subtracting, sampling, digitizing,outputting input signal 310 to pre-processor 312, performing any othersignal conditioning, or any combination thereof. In some embodiments,step 1202 may include pre-processor 312 calculating a derivative of thephysiological signal, averaging the physiological signal (e.g., timeaveraging, ensemble averaging), subtracting two physiological signals toproduce a single signal (e.g., subtracting noise background),calculating a ratio of two physiological signals to produce a singlesignal, performing any other suitable calculation, or any combinationthereof.

Step 1204 may include pre-processor 312 selecting one or more referencepoints of a physiological signal as described above. Step 1206 mayinclude pre-processor 312 selecting one or more fiducial points on thephysiological signal of step 1202, using the one or more referencepoints of step 1204 as described above. Step 1208 may includepre-processor 312 generating a fiducial signal based at least in part onthe selected fiducial points of step 1206. In some embodiments thefiducial signal includes a set of selected fiducial points (e.g., asshown by time series 730 of FIG. 7). At step 1208, pre-processor 312 mayaverage, filter, output (e.g., via a communications interface), store inmemory, or otherwise process, the fiducial signal.

Step 1210 may include processing system 300 processing the fiducialsignal of step 1208 for physiological information. In some embodiments,step 1210 may include processor 314, post-processor 316, or bothdetermining a physiological parameter such as, for example, pulse rate,respiration rate, blood pressure, any other suitable physiologicalparameter, or any combination thereof. In some embodiments, step 1210may include processor 314, post-processor 316, or both determining asignal metric such as, for example, an amplitude, a phase difference, anoffset, a signal to noise ratio, any other suitable signal metric of thefiducial signal, or any combination thereof. In some embodiments, step1210 may include processor 314, post-processor 316, or both storing aphysiological parameter value, signal metric, or both, in memory.

Step 1212 may include processor 314, post-processor 316, or bothevaluating the fiducial signal generated at step 1208 based at least inpart on the processed physiological information of step 1210. In someembodiments, the physiological information of step 1210 may be comparedwith reference physiological information (e.g., that may be stored inmemory, or provided by an independent monitoring device) to determine adifference in values. For example, a time series of physiologicalparameters may be calculated at step 1210 and may be compared with areference time series to determine a root mean square deviation (RMSD).The output of step 1212 may be a single metric (e.g., a RMSD value, aconfidence value), a set of metrics (e.g., an array of differences), aqualitative indicator (e.g., a discriminant such as “sufficientlyaccurate” or “poor accuracy”), any other suitable output form, or anycombination thereof.

Determination 1214 may include processor 314 or post-processor 316determining whether to repeat any or all of steps 1202-1212, perform anyother suitable steps, or any combination thereof. In some embodiments, aset of evaluations may be performed using determination 1214, and theset of evaluations may be compared at step 1216 to select a particularfiducial signal, and corresponding reference points and fiducial points.

In some embodiments, processing system 300 may perform step 1214 toevaluate a set of fiducial signals by repeating at least steps1206-1212, selecting different fiducial points at step 1206 for eachevaluation using a particular reference point(s) of step 1204. Forexample, pre-processor 312 may select various fiducial points for aparticular physiological signal and reference point(s), and processor314, post-processor 316, or both may evaluate the fiducial signalscorresponding to each of the various fiducial points, as shown by step1216.

In some embodiments, processing system 300 may perform step 1214 toevaluate a set of fiducial signals by repeating at least steps1204-1212, selecting different fiducial points at step 1206 for eachevaluation, based on a set of reference points of step 1204. Forexample, pre-processor 312 may select various combinations of referencepoints and fiducial points for a particular physiological signal, andprocessor 314 or post-processor 316 may evaluate the fiducial signalscorresponding to each of the various combinations, as shown by step1216.

In some embodiments, processing system 300 may perform step 1214 toevaluate a set of fiducial signals by repeating at least steps1202-1212, selecting different fiducial points at step 1206 for eachevaluation, based on a set of reference points of step 1204, for a setof physiological signals of step 1202. For example, pre-processor 312may select various combinations of reference points and fiducial pointsfor each physiological signal of the set of physiological signals, andprocessor 314 or post-processor 316 may evaluate the fiducial signalscorresponding to each of the various combinations, as shown by step1216.

Step 1216 may include processor 314 or post-processor 316 comparing aset of fiducial signals based at least in part on the evaluation of step1212. In some embodiments, step 1216 may include processor 314 orpost-processor 316 selecting the fiducial signal (along with thecorresponding reference points and fiducial points) corresponding to alowest RMSD value.

In an illustrative example, pre-processor 312 may receive a PPG signalincluding a set of successive pulse waves at step 1202. Pre-processor312 may select a set of reference points on the PPG signal correspondingto the successive peaks in the first derivative of the PPG signal atstep 1204. Also, at step 1204, pre-processor 312 may select a maximum inthe PPG signal located between each set of successive reference points.At step 1206, pre-processor 312 may select a fiducial pointcorresponding to each reference point, located a particular timeinterval away from the reference point, generating a set of fiducialpoints. Pre-processor 312 may generate a fiducial signal at step 1208,including the set of fiducial points of step 1206, and processor 314,post-processor 316, or both may determine physiological information suchas values of respiration information at step 1210. At step 1212,processor 314 or post-processor 316 may evaluate a series of values forrespiration information of step 1210 against a reference series ofvalues of respiration information by calculating a RMSD value.Processing system 300 may repeat steps 1206-1212 to generate a set offiducial signals and corresponding evaluations, using determination1214. At step 1216, processor 314 or post-processor 316 may compare theset of evaluations generated at step 1212, and select a particularfiducial signal along with corresponding fiducial points. Processingsystem 300 may use the time interval of the corresponding fiducialpoints as a pre-set time interval for subsequent analysis.

FIG. 13 shows an illustrative comparison for various fiducial pointselections on a particular PPG signal, in accordance with the presentdisclosure. The abscissa of graph 1300 is in units of time interval,increasing to the right. The ordinate of graph 1300 is in units of RMSDrelative to a reference RMSD. The RMSD value is calculated betweenrespiration information such as respiration rate derived from a fiducialsignal corresponding to each time interval, and a reference respirationrate (e.g., calculated by a reference analysis or program or calculatedusing an independent monitoring device). The maximum reduction is shownby relative RMSD 1302. In some embodiments, the time intervalcorresponding to RMSD 1302 may be used as a preset time interval tolocate fiducial points relative to a reference point. In someembodiments, a database of optimal time intervals may be created, andmapped across pulse rate, respiration rate, any other suitableparameter, or any combination thereof.

Any of the illustrative steps of flow diagrams 900-1200 may be combinedwith other steps, omitted, rearranged, or otherwise altered inaccordance with the present disclosure.

An example of a PPG signal changing its morphology over a series ofpulse cycles associated with a respiratory cycle is depicted in FIG. 14and FIG. 15. A respiratory cycle may typically have a longer period(lower frequency) than a pulse cycle and may span a number of pulseperiods. A respiratory cycle may span a number of pulse cycles based onthe relative respiration rate and pulse rate. An exemplary respiratorycycle 1402 may span four pulse periods as depicted in FIG. 14.Respiration may impact the shape of the pulse waveform, e.g., byamplitude and frequency modulation. For example, as depicted in FIG. 15,a first pulse associated with the respiratory cycle may have arelatively low amplitude as well as an obvious distinct dichrotic notchas indicated by point A. A second pulse may have a relatively highamplitude as well as a dichrotic notch that has been washed out asdepicted by point B. FIG. 15 depicts the pulses associated with point Aand B superimposed on the same scale for comparison. By the end of therespiratory cycle the pulse features may again be similar to themorphology of A. Respiration may have varied effects on the morphologyof a PPG signal other than those depicted in FIG. 15.

In some embodiments, pre-processor 312 may calculate morphology metricsto be used as inputs to determine respiration information. Pre-processor312 may receive a PPG signal as input signal 310 and may perform variousfiltering operations before calculating morphology metrics. Although aPPG signal may be described herein, it will be recognized thatmorphology metrics may be calculated from various other signals that mayinclude respiration information. The PPG signal may be filtered toremove any artifacts outside of the bandwidth of interest forrespiration. The PPG signal may be filtered in a manner to achieve a netzero phase change, for example by filtering once in the forwarddirection and then again in the reverse direction. An example filter maybe a third order Butterworth filter with a cutoff frequency of 7 Hz.Other filters may be used to remove artifacts outside of the bandwidthof interest for respiration, and filters may be chosen to remove varyingdegrees of artifacts. Other operations may also be performed, such asestablishing fiducial points as described herein.

Pre-processor 312 may calculate one or more sets of morphology metricvalues from the received signal. A PPG signal to be evaluated may be inthe form of samples having a corresponding sampling rate. For example, asampling rate of a PPG signal may be 76 Hz.

FIG. 16 depicts signals used for calculating morphology metrics from areceived PPG signal. The abscissa of each plot of FIG. 16 may berepresent time and the ordinate of each plot may represent magnitude.PPG signal 1600 may be a received PPG signal, first derivative signal1620 may be a signal representing the first derivative of the PPG signal1600, and second derivative signal 1640 may be a signal representing thesecond derivative of the PPG signal 1600. As will be described below,these signals may be utilized to calculate morphology metrics that maybe used as inputs by processor 314 or post-processor 316 to determinerespiration information such as respiration rate. Although particularmorphology metric determinations are set forth below, each of themorphology metric calculations may be modified in any suitable manner.Any of a plurality of morphology metrics may be utilized in combinationto determine respiration information.

Exemplary fiducial points 1602 and 1604 are depicted for PPG signal1600, and fiducial lines 1606 and 1608 demonstrate the location offiducial points 1602 and 1604 relative to first derivative signal 1620and second derivative signal 1640. The fiducial points may be determinedby pre-processor 312 as described herein. Fiducial points 1602 and 1604may define a fiducial-defined portion 1610 of PPG signal 1600. Thefiducial points 1602 and 1604 may define starting ending points fordetermining morphology metrics as described herein, and thefiducial-defined portion 1610 may be define a relevant portion of datafor determining morphology metrics as described herein. It will beunderstood that other starting points, ending points, and relativeportions of data may be utilized to determine morphology metrics.

An exemplary morphology metric may be a down metric. The down metric isthe difference between a first (e.g., fiducial) sample of afiducial-defined portion (e.g., fiducial defined portion 1610) of thePPG signal (e.g., PPG signal 1600) and a minimum sample (e.g., minimumsample 1612) of the fiducial-defined portion of the PPG signal. A downmetric may also be calculated based on other points of afiducial-defined portion. The down metric is indicative of physiologicalcharacteristics which are related to respiration, e.g., amplitude andbaseline modulations of the PPG signal. In an exemplary embodimentfiducial point 1602 defines the first location for calculation of a downmetric for fiducial-defined portion 1610. In the exemplary embodimentthe minimum sample of fiducial-defined portion 1610 is minimum point1612, and is indicated by horizontal line 1614. The down metric may becalculated by subtracting the value of minimum point 1612 from the valueof fiducial point 1602, and is depicted as down metric 1616.

A more detailed view of down metrics for multiple fiducial-definedportions is depicted in FIG. 17 for an amplitude modulated PPG signal.Each fiducial-defined portion has an associated down metric 1702, 1704,1706, 1708, and 1710. The values and change in values of the down metricmay be utilized as described herein to generate morphology metricsignals that are used as an input to determine respiration information,such as respiration rate. FIG. 18 depicts down metrics for a PPG signalthat includes baseline as well as amplitude modulation. Eachfiducial-defined portion has an associated down metric 1802, 1804, 1806,1808, and 1810. The values and change in values of the down metric maybe utilized as described herein to generate morphology metric signalsthat are used as an input to determine respiration information.

Another exemplary morphology metric may be a kurtosis metric for afiducial-defined portion. Kurtosis measures the peakedness of the firstderivative 1620 of the PPG signal. The peakedness is sensitive to bothamplitude and period (frequency) changes, and may be utilized as aninput to determine respiration information, such as respiration rate.Kurtosis may be calculated based on the following formulae:

$D = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\;( {x_{i}^{\prime} - \overset{\_}{x^{\prime}}} )^{2}}}$${Kurtosis} = {\frac{1}{{nD}^{2}}{\sum\limits_{i = 1}^{n}\;( {x_{i}^{\prime} - \overset{\_}{x^{\prime}}} )^{4}}}$where:x_(i)′=ith sample of 1^(st) derivative;x′=mean of 1st derivative of fiducial-defined portion;n=set of all samples in the fiducial-defined portion

Another exemplary morphology metric may be a delta of the secondderivative (DSD) between consecutive fiducial-defined portions, e.g., atconsecutive fiducial points. Measurement points 1642 and 1644 for a DSDcalculation are depicted at fiducial points 1602 and 1604 as indicatedby fiducial lines 1606 and 1608. The second derivative is indicative ofthe curvature of a signal. Changes in the curvature of the PPG signalare indicative of changes in internal pressure that occur duringrespiration, particularly changes near the peak of a pulse. By providinga metric of changes in curvature of the PPG signal, the DSD morphologymetric may be utilized as an input to determine respiration information,such as respiration rate. The DSD metric may be calculated for eachfiducial-defined portion by subtracting the second derivative of thenext fiducial point from the second derivative of the current fiducialpoint.

Another exemplary morphology metric may be an up metric measuring the upstroke of the first derivative signal 1620 of the PPG signal. The upstroke may be based on an initial starting sample (fiducial point) and amaximum sample for the fiducial-defined portion and is depicted as upmetric 1622 for a fiducial point corresponding to fiducial line 1606.The up metric may be indicative of amplitude and baseline modulation ofthe PPG signal, which may be related to respiration information asdescribed herein. Although an up metric is described herein with respectto the first derivate signal 1620, it will be understood that an upmetric may also be calculated for the PPG signal 1600 and secondderivative signal 1640.

Another exemplary morphology metric may be a skew metric measuring theskewness of the original PPG signal 1600 or first derivative 1620. Theskew metric is indicative of how tilted a signal is, and increases asthe PPG signal is compressed (indicating frequency changes inrespiration) or the amplitude is increased. The skewness metric isindicative of amplitude and frequency modulation of the PPG signal,which may be related to respiration information as described herein.Skewness may be calculated as follows:

${g\; 1} = {\frac{m_{3}}{m_{2}^{3/2}} = \frac{\frac{1}{n}{\sum\limits_{i = 1}^{n}\;( {x_{i} - \overset{\_}{x}} )^{3}}}{( {\frac{1}{n}{\sum\limits_{i = 1}^{n}\;( {x_{i} - \overset{\_}{x}} )^{2}}} )^{3/2}}}$where:x_(i)=ith sample;x=mean of the samples of the fiducial-defined portion;m₃=third moment;m₂=second moment; andn=total number of samples.

Another exemplary morphology metric may be a b/a ratio metric (i.e.,b/a), which is based on the ratio between the a-peak and b-peak of thesecond derivative signal 1640. PPG signal 1600, first derivative signal1620, and second derivative signal 1600 may include a number of peaks(e.g., four peaks corresponding to maxima and minima) which may bedescribed as the a-peak, b-peak, c-peak, and d-peak, with the a-peak andc-peak generally corresponding to local maxima within a fiducial definedportion and the b-peak and d-peak generally corresponding to localminima within a fiducial defined portion. For example, the secondderivative of the PPG signal may include four peaks: the a-peak, b-peak,c-peak, and d-peak. Each peak may be indicative of a respective systolicwave, i.e., the a-wave, b-wave, c-wave, and d-wave. On the depictedportion of the second derivative of the PPG signal 1640, the a-peaks areindicated by points 1646 and 1648, the b-peaks by points 1650 and 1652,the c-peaks by points 1654 and 1656, and the d-peaks by points 1658 and1660. The b/a ratio measures the ratio of the b-peak (e.g., 1650 or1652) and the a-peak (e.g., 1646 or 1648). The b/a ratio metric may beindicative of the curvature of the PPG signal, which demonstratesfrequency modulation based on respiration information such asrespiration rate. The b/a ratio may also be calculated based on thea-peak and b-peak in higher order signals such as PPG signal and firstderivative PPG signal 1620.

Another exemplary morphology metric may be a c/a ratio (i.e., c/a),which is calculated from the a-peak and c-peak of a signal. For example,first derivate PPG signal 1620 may have a c-peak 1626 which correspondsto the maximum slope near the dichrotic notch of PPG signal 1600, and ana-peak 1624 which corresponds to the maximum slope of the PPG signal1600. The c/a ratio of the first derivative is indicative of frequencymodulation of the PPG signal, which is related to respirationinformation such as respiration rate as described herein. A c/a ratiomay be calculated in a similar manner for PPG signal 1600 and secondderivative signal 1640.

Another exemplary morphology metric may be a i_b metric measuring thetime between two consecutive local minimum (b) locations 1650 and 1652in the second derivative 1640. The i_b metric is indicative of frequencymodulation of the PPG signal, which is related to respirationinformation such as respiration rate as described herein. The i_b metricmay also be calculated for PPG signal 1600 or first derivative signal1620.

Another exemplary morphology metric may be a peak amplitude metricmeasuring the amplitude of the peak of the original PPG signal 1600 orof the higher order derivatives 1620 and 1640. The peak amplitude metricis indicative of amplitude modulation of the PPG signal, which isrelated to respiration information such as respiration rate as describedherein.

Another exemplary morphology metric may be a center of gravity metricmeasuring the center of gravity of a fiducial-defined portion from thePPG signal 1600 in either or both of the x and y coordinates. The centerof gravity is calculated as follows:Center of gravity(x)=Σ(x _(i) *y _(i))/Σy _(i)Center of gravity(y)=Σ(x _(i) *y _(i))/Σx _(i)

The center of gravity metric of the x coordinate for a fiducial-definedportion is indicative of frequency modulation of the PPG signal, whichis related to respiration information such as respiration rate asdescribed herein. The center of gravity metric of the y coordinate for afiducial-defined portion is indicative of amplitude modulation of thePPG signal, which is related to respiration information such asrespiration rate as described herein.

Another exemplary morphology metric is an area metric measuring thetotal area under the curve for a fiducial-defined portion of the PPGsignal 1600. The area metric is indicative of frequency and amplitudemodulation of the PPG signal, which is related to respirationinformation such as respiration rate as described herein.

Although a number of morphology metrics have been described herein, itwill be understood that other morphology metrics may be calculated fromPPG signal 1600, first derivative signal 1620, second derivative signal1640, and any other order of the PPG signal. It will also be understoodthat any of the morphology metrics described above may be modified tocapture aspects of respiration information or other physiologicalinformation that may be determined from a PPG signal.

FIG. 19 depicts steps 1900 for generating a morphology metric signalfrom a PPG signal. The steps described in FIG. 19 may be performed bypre-processor 312, processor 314, a combination of pre-processor 312 andprocessor 314, or other portions or components of processing system 300.Although steps may be described as being performed by a particularcomponent of processing system 300, it will be recognized that suchdescription is exemplary only. Steps 1900 may be performed inalternative order, steps may be omitted, and additional steps may beinserted into the sequence of steps 1900.

At step 1902, an input signal 310 for computing a morphology metricrelated to respiration information such as respiration rate may bereceived, e.g., by pre-processor 312. The received signal may bereceived directly from a sensor and require further processing to beconverted into a digital signal, or may be a digital signal that haspreviously been processed, e.g., a sampled digital output received froma pulse oximetry device. An exemplary received signal may be a PPGsignal from a pulse oximetry device, which may be sampled at a samplingrate, for example, 76 Hz. The received signal may encompass a samplingwindow such as 5 seconds. Pre-processor 314 may locate reference pointsand fiducial points to identify one or more fiducial-defined portions,each of which may be utilized to calculate one or more morphologymetrics which may be used to generate one or more morphology metricsignals for an analysis window (e.g., a 45 second analysis window of the9 most recent sampling windows) as described herein. The received signalmay also be filtered to remove artifacts outside of the bandwidth ofinterest for respiration. The filter may be a low pass filter or anyother filter that removes information outside of the bandwidth ofinterest. The filter may be implemented in any suitable manner, e.g.,with a third order butterworth filter having a cutoff frequency of 7 Hz.The cutoff frequency may be any frequency appropriate to recognizemorphology features related to respiration, and may vary based onphysiological parameters such as heart rate. In order to maintainmorphology features, the feature set may be filtered in a manner toachieve a zero net phase change, e.g., by filtering the PPG signaltwice, once in each direction.

At step 1904, pre-processor 312 may calculate morphology metric valuesfrom the received signal. Morphology metric values may be calculated foreach fiducial-defined portion of the analysis window, e.g., eachfiducial-defined portion of the 45 second analysis window. A morphologymetric may be any measurement of the form or structure of a signal thatmay relate to a given physiological characteristic such as respirationinformation. In an exemplary application, the morphology metric mayrelate to respiration information such as respiration rate and may bedetermined from a sampled PPG signal. Morphology metrics may includedown metric, kurtosis metric, DSD metric, up metric, skew metric, b/aratio metric, c/a ratio metric, i_b metric, peak amplitude metric,center of gravity metric, and area metric, and may be calculated asdescribed herein. As described herein, multiple morphology metric valuesmay be calculated from the PPG signal, the first and second derivativeof the PPG signal, and other order derivative of the PPG signal, or fromany combination thereof.

At step 1906, pre-processor 312 may determine a usable portion of theinput signal 310. Portions of the received signal may include sampleswith values that are unlikely to reflect actual values as a result ofinaccurate measurement, user error, or other factors. Input signal 310may be analyzed to identify divergences in the signal baseline, motionartifacts, divergences in pulse period, and any other signal featuresthat may indicate inaccurate measurement, user error, or other factors.Based on this analysis, pre-processor 312 may identify portions of theinput signal 310 to be ignored by processor 314 in calculating valuessuch as respiration information. Only those portions of the calculatedmorphology metric values that correspond to the usable portion of theinput signal may be provided to processor 314. Pre-processor 312 mayalso calculate additional values relating to the usable portion of thesignal, such as variability of the signal amplitude, variability of thepulse period, an average age for the usable portion of the signal, andother parameters relating to the quality of the PPG signal. Theamplitude variability, pulse period variability, age, and otherparameters may be provided to processor 314, post-processor 316, orboth.

At step 1908, one or more sets of the received morphology metric valuesmay be attenuated by processor 314 to adjust outliers. In an exemplaryembodiment, pre-processor 312 may calculate a series of morphologymetric values for a set of fiducial-defined portions. A threshold may becalculated for determining which values should be attenuated, and anattenuation value may be determined to attenuate outliers. Theattenuation value may modify outliers in any manner, such as with acutoff value or by reducing the outliers based on a percentage or otherformula. In an exemplary embodiment, the attenuation value may be equalto the threshold and any outliers that exceed the threshold may be setto the threshold. The threshold may be calculated based oncharacteristics of the underlying signal, the morphology metrics,empirically determined values, any other suitable technique, or anycombination thereof. The threshold may be the same for positive andnegative values or each polarity may have its own threshold andattenuation value. An exemplary threshold may be based on the standarddeviation of a series of calculated morphology metric values multipliedby a constant. The exemplary attenuation value may be equal to thethreshold, and the threshold and attenuation values may be the same fornegative values.

At step 1910, the attenuated series of morphology metric values may beinterpolated by processor 314 to derive a morphology metric signal thatmay be indicative of respiration information such as respiration rate.An exemplary interpolation technique may be to perform linearinterpolation on the time series of calculated morphology metrics. Itwill be understood that any suitable interpolation technique may be usedto derive the morphology metric signal, such as higher ordercurve-fitting techniques. The interpolation may be performed at a ratedifferent from the sampling rate of the original PPG signal that formedthe basis of the morphology metric. For example, morphology metricscalculated from an exemplary 76 Hz PPG input may be interpolated at a ⅙of the original rate, or at 12.66 Hz, to create an interpolatedmorphology metric signal.

At step 1912, the interpolated morphology metric signal may be filteredby processor 314 to smooth the signal and remove information that isoutside the interest for respiration. An exemplary filter may be aband-pass filter that removes information outside of the bandwidth ofinterest for respiration. For three exemplary sets of morphologymetrics, the exemplary pass bands may be 0.15 Hz-0.9 Hz (down metric),0.07-0.7 Hz (kurtosis metric), and 0.07-0.7 Hz (DSD metric). The featureset may be filtered twice, once in each direction, to achieve a zero netphase change. It will be understood that that the filter may beimplemented in any suitable manner, and that any suitable pass bands maybe used for the filter.

At step 1914, the filtered morphology metric signal may be downsampledby processor 314 to a sampling rate to be used as an input to deriverespiration information such as respiration rate. For example, thefiltered morphology metric signal may be downsampled to a lowerfrequency value such as 2.53 Hz. This sampling rate may be common formultiple morphology metrics, such that different morphology metrics maybe more easily compared to determine respiration information such asrespiration rate.

Steps 1900 may be repeated to generate each morphology metric signal. Inan exemplary embodiment, steps 1900 may be repeated to generate a downmetric signal, a kurtosis metric signal, and a DSD metric signal. Itwill be understood that any number or combination of morphology metricsignals may be generated for the morphology metrics described herein.

FIG. 20 depicts a set of plots 2000, 2010, 2020, and 2030 depictingaspects of the signal processing steps for calculating a morphologymetric signal from a PPG signal as described herein. Specifically, FIG.20 depicts an exemplary calculation of a down metric signal from anexemplary PPG signal 2002 in accordance with the steps described herein.Although FIG. 20 depicts an example of determining a down metric, eachmorphology metric may be processed in a similar manner. Alternatively,each morphology metric may have its own process or set of parameters toderive a signal useful for determining respiration information from aPPG signal. With respect to any morphology metric, additional operationssuch as filtering and calculation steps may be performed, and stepsdiscussed below may be omitted.

PPG signal 2002 may be received, e.g., by pre-processor 312 as inputsignal 310, as digital data with a sampling rate based on the output ofa device such as a pulse oximeter. Input signal may be streamed topre-processor 312 or may be received in discrete sampling windows, e.g.,every 5 seconds of data. Plot 2000 may be depicted in units of sampleson the abscissa and magnitude on the ordinate, based on a sampling rateof 76 Hz. Although 76 Hz is an exemplary sampling rate, any samplingrate may be utilized to provide an interface with a pulse oximeter orother device providing the PPG signal. Plot 2000 may depict a portion ofan analysis window used to generate morphology metric signals. Anexemplary analysis window may include 45 seconds of samples, andmorphology metrics may be recalculated for the analysis window for eachnew 5 second sampling window of PPG values that is received.

Plot 2000 depicts a portion of an analysis window for which a morphologymetric signal may be determined from the PPG signal. Fiducial points2004 may be calculated as described herein and may be utilized indetermining a down metric for PPG signal 2002 for each fiducial definedportion. Although a down metric is described herein, PPG signal 2002(and the first and second derivative of PPG signal 2002) may be utilizedto determine other morphology metrics as described herein. The fiducialpoint 2004 locations depicted in plot 2000 are exemplary, and otherfiducial point 2004 locations may be used to determine the down metricand other morphology metrics.

A down metric may be calculated for each fiducial-defined portion of thePPG signal as described herein, e.g., by calculating the differencebetween the amplitude at the fiducial point and the lowest-amplitudesample for each fiducial-defined portion. The resulting morphologymetric values may be provided to processor 314, and any unusableportions of the analysis window may be removed as described herein. Inthe exemplary embodiment depicted in FIG. 20, the complete set of downmetric values depicted in plot 2010 may be provided to processor 314 asa portion of an analysis window. Plot 2010 is depicted in units ofsamples on the abscissa and magnitude on the ordinate, based on theoriginal sampling rate of the received PPG signal 2002, e.g., 76 Hz.Each down metric 2012 may be located at the starting fiducial point foreach respective fiducial-defined portion. Once the down metric valuesare calculated, those values may be attenuated as described herein. Astandard deviation may be calculated for the down metric values. Athreshold may be based on that standard deviation multiplied by aconstant, e.g., 1.6. Any down metric values exceeding 1.6*(standarddeviation of down metrics) may be attenuated to the threshold value. Itwill be recognized that other suitable threshold values and attenuationvalues may be utilized as described herein.

A linear interpolation of the down metric values may then be performed.The linear interpolation may be at a lower frequency than the 76 Hz PPGinput signal, e.g., at 12.66 Hz. Plot 2020 depicts a linearinterpolation of the attenuated down metric values. The interpolatedvalues may then be filtered to remove information outside of thebandwidth of interest as described herein. For example, a window ofinterest may capture respiration rate information ranging from 3 to 50breaths per minute, e.g., using a bandpass filter. The resultingmorphology metric signal may be downsampled to a lower frequency valuesuch as 2.53 Hz. This sampling rate may be a common for multiplemorphology metrics, such that different morphology metrics may becompared on the same scale to determine respiration information such asrespiration rate. It will be understood that downsampling may beaccomplished in any suitable manner, and that the resulting signal mayhave any suitable frequency. Plot 2030 depicts the resulting morphologymetric signal.

In an exemplary embodiment, pre-processor 312 may perform a number oftests to determine whether any portions of the information calculatedfrom the analysis window (e.g., one or more morphology metricscalculated for a 45 second analysis window) should be ignored,discarded, or deemphasized, and calculate a number of related values.FIG. 21 depicts steps for determining which portions of the analysiswindow include useable data. The steps depicted in FIG. 21 may beexecuted in any order, any or all of the steps may be omitted, andadditional steps may be included.

At step 2102, pre-processor 312 may identify any large baseline shiftsthat may result in unusable or degraded performance for the calculationof respiration information. The PPG signal may be filtered in anysuitable manner. For example, the original PPG signal may be filteredwith a 3^(rd) order Butterworth filter about a region of interest suchas 0.07 to 0.7 Hz. To achieve a zero phase change, the signal may befiltered twice, once in each direction. The absolute value of eachsample of the resulting signal may be compared to a thresholdcorresponding to a baseline shift, for example, at 2.9 multiplied by thestandard deviation of the baseline signal. It will be understood thatany suitable threshold may be used and that the threshold may be basedon any suitable baseline other than the standard deviation. Any samplesthat exceed the threshold may indicate areas of data to be ignored ordeemphasized in future calculations such as for respiration information.The portion of the data to be ignored or deemphasized may be determinedin any suitable manner. For example, pre-processor 312 may identify thelargest section of the resulting signal that does not include anyoutliers. That portion of the signal may be used for subsequentcalculations, and in some instances an additional buffer section (e.g.,5 seconds) may be removed from the usable portion adjacent to anyidentified outliers.

At step 2104, pre-processor 312 may identify invalid artifacts orsamples in the usable portion identified in step 2102. It will beunderstood that the presence of an invalid artifact or sample may bedetermined in any suitable manner. For example, a last artifact orinvalid sample flag may be received with the PPG signal as describedherein. If either flag is asserted during a portion of the usableportion of the PPG signal from step 2102, portions of the PPG signalcorresponding to the last artifact or invalid sample flag may be removedfrom the usable portion in any suitable manner. For example, portionscorresponding to an invalid artifact or sample may be removed byignoring the artifact or invalid sample event and any portions of theusable signal that occur prior to the artifact or invalid sample event.

At step 2106, pre-processor 312 may identify any out of range pulsevalues within the usable portion of the analysis window. The appropriaterange may be determined in any suitable manner. For example, a validpulse rate range may be 40 to 170 beats per minute. Pre-processor 312may maintain a running average of the pulse rate corresponding to aportion of the analysis windows, e.g., for each 5 second samplingwindow. If at any time the running average is less than the minimumpulse rate (e.g., 40 beats per minute) or is greater than the maximumpulse rate (e.g., 170 beats per minute), portions of the overallanalysis window that correspond to the out of range portion may beignored or deemphasized in any suitable manner, e.g., by ignoring alldata that precedes the out of range portion.

At step 2108, pre-processor 312 may calculate variability metrics forthe remaining usable portion of the analysis window (e.g., after steps2102-2106) for subsequent use by processor 314, post-processor 316, orboth. An amplitude variability metric may be calculated in any suitablemanner. For example, the amplitude variability metric may be calculatedby subtracting the minima from the maxima for each fiducial-definedportion. An amplitude difference may be calculated for each set ofconsecutive fiducial-defined portions. Once all of the amplitude andamplitude difference values are calculated, an amplitude variabilitymetric may be the sum of the amplitude difference values divided by thesum of the amplitude values. Calculation of the amplitude variabilitymetric may be performed as follows:

amp(i) = max   sample  in  ith  pulse − min   sample  in  ith  pulseampDiff(i) = amp(i + 1) − amp(i)${{Amplitude}\mspace{14mu}{Variability}} = \frac{\sum\limits_{i = 1}^{n - 1}{{ampDiff}(i)}}{\sum\limits_{i = 1}^{n - 1}{{amp}(i)}}$

A period variability metric may be based on a period which and may becalculated in any suitable manner. For example, a period variabilitymetric may be calculated for each fiducial-defined portion. A perioddifference may be calculated for each set of consecutivefiducial-defined portions. Once all of the period and period differencevalues are calculated, a period variability metric may be the sum of theperiod difference values divided by the average pulse period over the 45second analysis window. Calculation of the period variability metric maybe performed as follows:

     perDiff(i) = period(i) − period(i + 1)${{Pulse}\mspace{14mu}{Period}} = {( \frac{1}{dt} )*\frac{60}{{Mean}\mspace{14mu}{Non}\text{-}{Zero}\mspace{14mu}{Pulse}\mspace{14mu}{Rate}\mspace{14mu}{over}\mspace{14mu}{last}\mspace{14mu} 45\mspace{14mu}{seconds}}}$$\mspace{79mu}{{{Period}\mspace{14mu}{Variability}} = \frac{\sum\limits_{i = 1}^{n - 1}{{perDiff}(i)}}{{Pulse}\mspace{14mu}{Period}}}$     dt = Sample  Period = .0132  ms

At step 2110, pre-processor 312 may identify any portions of the usableportion of the analysis window where adjacent fiducial-defined portionshave a pulse period difference that exceeds a threshold. A threshold forthe pulse period difference may be determined in any suitable manner.For example, if the difference between the pulse period for twoconsecutive fiducial-defined portions exceeds 30% of the average pulseperiod for the analysis window, any data corresponding to thesefiducial-defined portions may be ignored, e.g., by excluding any data ofthe usable portion of the analysis window that occurs prior to theinvalid pulse period.

At step 2112, pre-processor 312 may calculate the age of the usableportion of the analysis window. The age of the usable portion of theanalysis may be calculated in any suitable manner. For example, if thefull analysis window of 45 seconds is usable, the age of the analysiswindow may be 22.5 seconds. As another example, if the most recent 10seconds of the analysis window are not usable, and only the prior 35seconds of the analysis window are usable, the age may be 27.5 seconds,i.e., 10 seconds (first valid sample) plus 45 seconds (last validsample) divided by 2.

Steps for generating respiration information such as a respiration rateare depicted in FIGS. 22A and 22B. In an exemplary embodiment, processor314 may perform the steps described herein, however it will beunderstood that some or all of the steps may be performed bypre-processor 312, post-processor 316, or other suitable processingcircuitry. In an exemplary embodiment processor 314 may receive one ormore sets of morphology metric values from pre-processor 312. In anexemplary embodiment processor 314 may receive sets of morphology metricvalues for the down metric, kurtosis metric, and DSD metric. It will beunderstood that any number of sets of morphology metric values may bereceived, and that the types of morphology metrics may be any suitablemetrics as described herein. In an exemplary embodiment, at step 2202processor 314 may derive a down metric signal as described herein,including attenuating outliers, interpolating the samples to generate asignal, band pass filtering the signal, and downsampling. Processor 314may also generate a kurtosis metric signal at step 2204 and a DSD metricsignal at step 2206 in a similar manner.

At steps 2208, 2210, and 2212 an autocorrelation sequence may begenerated for each morphology metric signal, e.g., the down metricsignal, kurtosis metric signal, and DSD metric signal, respectively.Autocorrelation is the cross-correlation of a signal with itself, and tothe extent that the underlying signal includes regular or repeatingpatterns the peaks of the autocorrelation may correspond to periodiccomponents of the underlying signal. The autocorrelations of themorphology metric signals may be utilized to determine respirationinformation such as respiration rate as described herein. However, asingle autocorrelation sequence corresponding to a singleautocorrelation metric may not provide sufficient information todetermine the respiration information with a desired accuracy orcertainty. Accordingly, a plurality of autocorrelation sequencescorresponding to respective morphology metric signals may be utilized todetermine respiration information. The formula for the autocorrelationis the following:R _(xx)(m)=Σ_(nεs) x(n)x(n−m), for m=−M, . . . ,Mwhere:S=the signal support of the finite segment;N=the maximum lag computed for the autocorrelation.

For real signals with a maximum point located at the central point ofthe autocorrelation (i.e., where the signal is being compared directlywith itself without any time lag) the autocorrelation sequence may besymmetric about the central point. Accordingly, it may be possible tocalculate the autocorrelation for one half of the overall lag about zero(e.g., from −M to 0, or from 0 to M) and duplicate the result about thecentral point. Accordingly, the autocorrelation sequence may becalculated as follows:

${R_{xx}(m)} = \{ \begin{matrix}{{\sum\limits_{n = 0}^{\min{({{\max{({{L - m},0})}},L})}}{{x( {n + m} )}{x(n)}}},} & {{{{for}\mspace{14mu} m} = 0},\ldots\mspace{14mu},M} \\{{R_{xx}( {- m} )},} & {{{{for}\mspace{14mu} m} = {- M}},\ldots\mspace{14mu},{- 1}}\end{matrix} $

At steps 2214, 2216, and 2218 an autocorrelation metric may becalculated for each of the autocorrelation sequences, which in anexemplary embodiment may be a down metric autocorrelation sequence,kurtosis metric autocorrelation sequence, and DSD metric autocorrelationsequence. An autocorrelation metric may quantify the regularity orperiodicity of the underlying morphology metric signal based on theautocorrelation sequence. FIG. 23 depicts an exemplary autocorrelationsequence 2302. The abscissa of FIG. 23 is in units of seconds and spansan exemplary 45 second analysis window for a complete autocorrelationsequence, while the ordinate may represent the magnitude of theautocorrelation sequence. As described above, the autocorrelationsequence may be symmetric about the central or maximum point.

The central point of the autocorrelation sequence corresponds to theunderlying morphology signal compared with itself without a time lag.The remaining points of the autocorrelation sequence may indicate theregularity or periodicity of the signal. It will be understood that anysuitable analysis of the autocorrelation signal may be performed toanalyze the regularity or periodicity of the underlying signal. Forexample, the autocorrelation sequence will have larger magnitude(positive or negative) repeating peaks if a signal is regular orperiodic. Accordingly, the peaks may be utilized to calculate anautocorrelation metric which is representative of the regularity orperiodicity of the morphology metric signal. In an exemplary embodimentthe first four local minima 2304, 2306, 2308, and 2310 to the right ofthe central point may be selected. Because the autocorrelation sequenceis symmetric, local minima to the left of the central point should beidentical. If there are fewer than four local minima (e.g., due to a lowrespiration rate or if the usable portion of the morphology metricsignal is limited) then all of the local minima to the right of thecentral point may be used to calculate the autocorrelation metric.

In an exemplary embodiment the local minima 2304, 2306, 2308, and 2310may be normalized in any suitable manner, e.g., by dividing themagnitude of each of local minima 2304, 2306, 2308, and 2310 by themagnitude of the central point. A threshold may be calculated in anysuitable manner. Any normalized local minima that do not exceed athreshold may be discarded. It will be understood that theautocorrelation metric may be calculated in any suitable manner from thenormalized minima. For example, the resulting normalized local minimamay be averaged to calculate the autocorrelation metric. Anautocorrelation metric may be calculated in this manner for eachautocorrelation sequence.

Referring again to FIG. 22A, once the autocorrelation metrics arecalculated at steps 2214, 2216, and 2218, each of the autocorrelationsequences may be filtered with previous filtered autocorrelationsequences 2226, 2228, and 2230 at steps 2220, 2222, and 2224. Exemplaryprevious filtered autocorrelation sequences 2226, 2228, and 2230 may bethe filtered autocorrelation sequences for a previous set of receiveddata, e.g., the 45 second analysis window established by the previous 5seconds of received PPG data. Filtering of the autocorrelation sequencesmay be performed in any suitable manner. In an exemplary embodiment,processor 314 may calculate a filter weight for each autocorrelationsequence based on the autocorrelation metric and a time ratio. The timeratio may be based on the length of the usable portion of the analysiswindow divided by the length of the analysis window. The filter weightmay be calculated for each autocorrelation sequence by multiplying eachautocorrelation metric and the time ratio. If the resulting filterweight exceeds a predetermined limit such as 1, the filter weight may beset to the predetermined limit. In addition, because the filter is aninfinite impulse response filter, the filter weight (wt) may be phasedin during startup. The filter weight may be phased in using any suitabletechnique, such as the following:

${wt} = {\max( {{wt},\frac{1}{{number}\mspace{14mu}{of}\mspace{14mu}{points}\mspace{14mu}{processed}}} )}$

For example, for the first point to be processed, the weight will be setto 1, since the filter weight is also limited to 1. For the secondpoint, the filter weight will be compared to 0.5, and so on until thefilter weight exceeds the threshold and is used to calculate theremaining points of the filtered autocorrelation sequence. Once thefilter weight is calculated, each point of the autocorrelation sequencemay filtered in an infinite impulse response filter with thecorresponding value from the previous filtered correlation sequence asfollows:FilteredSeq=wt*NewSeq+(1−wt)*PrevSeqwhere:FilteredSeq=Filtered Autocorrelation Sequence;wt=Filter Weight;NewSeq=Autocorrelation Sequence;PrevSeq=Previous Filtered Autocorrelation Sequence.

Processor 314 may also calculate a sequence age for each filteredautocorrelation sequence. The sequence age may be calculated in anysuitable manner. In an exemplary embodiment, the sequence age may bebased on the filter weight, the age of the previous filteredautocorrelation sequence, and the age of the autocorrelation sequence asfollows:SequenceAge=wt*CurrentAge+(1−wt)*PrevAgewhere:SequenceAge=Filtered Autocorrelation Sequence Age;wt=Filter Weight;CurrentAge=Autocorrelation Sequence Age;PrevAge=Previous Filtered Autocorrelation Sequence Age.

Once the filtered autocorrelation sequences and corresponded sequenceages have been calculation, processing may continue as depicted in FIG.22B. Processor 314 may calculate a combination weight for each of thefiltered autocorrelation sequences at steps 2232, 2234, and 2236. Eachof the filtered autocorrelation sequences may be based on a differentmorphology metric signal and each morphology metric signal capturesrespiration information in a different manner. A combination weight foreach filtered autocorrelation sequence may be calculated to adjust therelative emphasis of each of the filtered autocorrelation sequences incalculating respiration information. The combination weight may becalculated in any suitable manner to modify the relative weight of eachof a plurality of autocorrelation sequences in a manner to accuratelydetermine respiration information. In an exemplary embodiment acombination weight may be representative of the regularity of theautocorrelation metric as well as consistency of the filteredautocorrelation sequence over time. For each filtered autocorrelationsequence the weight of the current sequence (w_(new)) may be calculatedbased on the autocorrelation metric and a Pearson correlationcoefficient:W _(new)=(A _(x) +r)¹²where:A_(x)=autocorrelation metric;r=Pearson correlation coefficient.

The Pearson correlation coefficient may be calculated as follows:

$r = {\frac{1}{n - 1}{\sum\limits_{i = 1}^{n}{( \frac{X_{i} - \overset{\_}{X}}{s_{X}} )( \frac{Y_{i} - \overset{\_}{Y}}{s_{Y}} )}}}$where:X=current filtered autocorrelation sequence;Y=previous filtered autocorrelation sequence;S_(X), S_(Y)=sample standard deviation; andX,Y=sample mean.

$( \frac{X_{i} - \overset{\_}{X}}{s_{X}} ),{( \frac{Y_{i} - \overset{\_}{Y}}{s_{Y}} ) = {{standard}\mspace{14mu}{score}}}$

Once the weight of the current sequence is calculated, the combinationweight may be calculated as follows:w _(C)=(b*w _(new)+(1−b)*w _(Cprev))*tRatiowhere:w_(C)=combination weight;w_(new)=weight of the current sequence;w_(Cprev)=weight of the previous sequence;b=0.01*tRatio; andtRatio=time ratio.

A combination weight w_(c-D) for the filtered autocorrelation sequenceassociated with the down metric signal may be calculated at step 2232, acombination weight w_(C-K) for the filtered autocorrelation sequenceassociated with the kurtosis metric signal may be calculated at step2234, and a combination weight w_(C-DSD) for the filteredautocorrelation sequence associated with the DSD metric signal may becalculated at step 2236. It will be understood that an autocorrelationmetric may be calculated in a similar manner for any otherautocorrelation sequence associated with any other morphology metric. Atstep 2238, processor 314 may generate a combined autocorrelationsequence from the filtered autocorrelation sequences based on thecombination weights. For example, the combined autocorrelation sequencemay be generated according to the following:

${{Combined}\mspace{14mu}{Sequence}} = \frac{( {{w_{C - D}*S_{D}} + {w_{C - K}*S_{K}} + {w_{C - {DSD}}*S_{DSD}}} )}{( {w_{C - D} + w_{C - K} + w_{C - {DSD}}} )}$where:w_(C-D)=combination weight for down metric sequence;w_(c-K)=combination weight for kurtosis sequence;w_(C-DSD)=combination weight for DSD sequence;S_(D)=filtered down sequence;S_(K)=filtered kurtosis sequence; andS_(DSD)=filtered DSD sequence.

Processor 314 may calculate a combined autocorrelation age for thecombined autocorrelation sequence. The combined autocorrelation age maybe calculated in any suitable manner. In an exemplary embodiment thecombined autocorrelation age may be based on the previously calculatedsignal age and combination weight for each of the autocorrelationsequences as follows:

${CombinedAge} = \frac{( {{w_{C - D}*{Age}_{D}} + {w_{C - K}*{Age}_{K}} + {w_{C - {DSD}}*{Age}_{DSD}}} )}{( {w_{C - D} + w_{C - K} + w_{C - {DSD}}} )}$where:w_(c-D)=combination weight for down metric sequence;w_(c-K)=combination weight for kurtosis sequence;W_(C-DSD)=combination weight for DSD sequence;Age_(D)=age of down sequence;Age_(K)=age of kurtosis sequence; andAge_(DSD)=age of DSD sequence.

At step 2240 processor 314 may derive respiration information from thecombined autocorrelation sequence. Respiration information may bederived from the combined autocorrelation sequence in any suitablemanner. In one exemplary embodiment of deriving respiration informationfrom the combined autocorrelation sequence, processor 314 may utilize awavelet transform to derive respiration information. Although a numberof wavelet parameters may be utilized to derive respiration informationfrom the combined autocorrelation sequence, exemplary parameters aredescribed below. An exemplary wavelet transform method may be acontinuous wavelet transform and an exemplary wavelet may be a realMorlet wavelet. Scale parameters may be selected in any manner thatcaptures respiration information. For example, a characteristicfrequency range may be selected based on a range of frequency forrespiration, such as 0.05 Hz (3 breaths per minute) to 1.0 Hz (60breaths per minute). The scale resolution may be selected to determinethe number of scales that are generated by the continuous wavelettransform. A smaller scale resolution (i.e., a larger number of scalescorresponding to the characteristic frequency range of the correspondingwavelets) may be more computationally intensive but may yield greateraccuracy in deriving respiration information. In an exemplary embodiment60 scales may correspond to the characteristic frequency range of thecorresponding wavelets.

Steps for generating a scalogram from the combined autocorrelationsequence are depicted in FIG. 24. In the discussion of the technologywhich follows herein, the “scalogram” may be taken to include allsuitable forms of resealing including, but not limited to, the originalunsealed wavelet representation, linear resealing, any power of themodulus of the wavelet transform, or any other suitable resealing. Inaddition, for purposes of clarity and conciseness, the term “scalogram”shall be taken to mean the wavelet transform, T(a,b) itself, or any partthereof. For example, the real part of the wavelet transform, theimaginary part of the wavelet transform, the phase of the wavelettransform, any other suitable part of the wavelet transform, or anycombination thereof is intended to be conveyed by the term “scalogram.”The steps described are exemplary only, and it will be understood thatsome of the steps may be rearranged or omitted, and that additionalsteps may be added. These steps may be repeated for each scale togenerate the scalogram. It will be understood that the term scalogrammay refer to any suitable scalogram or modification thereof, e.g., acombined sum scalogram or sum scalogram vector as described herein.Although the steps of FIG. 24 are described as being performed byprocessor 314, it will be understood that one or more of pre-processor312, post-processor 316, or other processing circuitry may perform someor all of the processing steps. At step 2402, processor 314 may selectthe scale to be generated. In an exemplary embodiment, the first scalemay be associated with the highest characteristic frequency of thecharacteristic frequency range, e.g., 1.0 Hz. At step 2404, processor314 may perform cyclic padding on the combined autocorrelation sequence.

Cyclical padding is depicted in more detail in FIG. 25. Signal 2502 mayrepresent the combined autocorrelation sequence. It may be desirable toprovide padding on either or both sides of signal 2502 for purposes ofperforming the wavelet transform, e.g., to account for edge effects whenperforming a convolution with the mother wavelet. Padding may beperformed in any suitable manner. In an exemplary embodiment, paddingmay be performed by repeating a portion of the original signal andattaching the repeated portion to the signal. For example, padding 2504may correspond to the later samples of signal 2502 and may attach to thebeginning of signal 2502. In an exemplary embodiment padding 2504 may beequal to the final 50% of signal 2502. Padding 2506 may correspond tothe earlier samples of signal 2502 and may attach to the end of signal2502. In an exemplary embodiment padding 2506 may be equal to theinitial 50% of signal 2502.

It may also be desirable to dynamically scale the padding to correspondto the length of the wavelet. Dynamic scaling may be performed in anysuitable manner to modify the padding length relative to the waveletlength. The wavelet length increases with higher scale values.Accordingly, in an exemplary embodiment, for each scale value a new padlength may be calculated and a new padded signal created based on thewavelet length. For example, an original signal of length N may beexpressed as follows:x=[x(0),x(1),x(2), . . . x(N−1)]

If m represents the amount of padding, the signal with padding may beexpressed as follows:x=[X(N−m),x(N−m+1), . . . x(N−1),x(0),x(1), . . . x(N−1),x(0),x(1) . . .x(m−1)]

The resulting signal length L for the padded signal is 2*m+N. Dynamicscaling may modify the m term based on the wavelet length. In anexemplary embodiment, the padding length may be equal to 50% of thewavelet length. It will be understood that other relationships betweenthe padding length and wavelet length may be selected.

Referring again to FIG. 24, at step 2406 processor 314 may perform awavelet transform such as a continuous wavelet transform. The continuouswavelet transform of a signal x(t) in accordance with the presentdisclosure may be defined as:

${T( {a,b} )} = {\frac{1}{\sqrt{a}}{\int_{- \infty}^{\infty}{{x(t)}{\psi^{*}( \frac{t - b}{a} )}\ {\mathbb{d}t}}}}$where:a=scale value;b==shift parameter; andψ(t)=wavelet function and * denotes complex conjugate.

In an embodiment the wavelet transform may be defined as:

${{WT}( {a,b} )} = {\frac{1}{\sqrt{a}}{\sum\limits_{n \in S}^{\;}{{x_{new}(n)}{\psi^{*}( \frac{n - b}{a} )}\Delta\; T}}}$where:ΔT=sampling interval;x_(new)=padded combined autocorrelation sequence; andS=support of the signal.

If a real Morlet wavelet is used, it may not be necessary to utilize thecomplex conjugate of the wavelet function.

FIG. 26 depicts aspects of the convolution of the padded combinedautocorrelation sequence 2602 with the wavelet function 2604. It will beunderstood that convolution of the padded combined autocorrelationsequence 260 with the wavelet function 2604 may be performed in anysuitable manner. In an exemplary embodiment, padded combinedautocorrelation sequence 2602 may have N samples and wavelet function2604 may have M samples. The convolution may be depicted as the paddedcombined autocorrelation sequence 2602 incrementally translating acrossthe wavelet function 2604 and being combined where the functions overlapat each translation point. Region 1 of FIG. 26 depicts an example of afirst region where there is not complete overlap between the signals,i.e., the first M−1 samples of the convolution. Region 2 of FIG. 26depicts examples of a second region in which there is complete overlapof the signals, i.e., the M through N−1 samples of the convolution.Region 3 of FIG. 26 depicts an example of a third region where there isnot complete overlap between the signals, i.e., the N through M+N−2samples of the convolution.

At the edges of the convolution (e.g., some or all of regions 1 and 3 asdescribed above) there may be an undesirable edge effect. The highfidelity portion of the convolution result may be located in the centralportion of the convolution. It will be understood that the edge effectmay be compensated for in any suitable manner. In an exemplaryembodiment, only some portion of the central portion of the signal maybe selected for the convolution result, such as the middle N samples orthe portion of the samples corresponding to the combined autocorrelationsequence prior to padding. In the latter example, any edge effects mayoccur only for the padded portions of the combined autocorrelationsequence based on the pad size being equivalent to one half of thewavelet size. For ease of calculation, only the desired portions of theconvolution may be calculated.

Referring again to FIG. 24, the result of the convolution may be summedto generate a sum scalogram corresponding to the particular scale atstep 2408. It will be understood that the sum scalogram may becalculated in any suitable manner. The sum scalogram may be utilized todetermine respiration information as described herein. At step 2410,processor 314 may determine if there are additional scales to process.If so, another scale may be selected at step 2402 and the process mayrepeat until all scales are processed. The result may be a combined sumscalogram.

Referring again to FIG. 22B, once the continuous wavelet transform hasbeen performed and the combined sum scalogram generated, processor 314may estimate respiration information at step 2242. It will be understoodthat respiration information may be estimated form the combined sumscalogram in any suitable manner. In an exemplary embodiment, processor314 may sum across all scales of the combined sum scalogram to create asum scalogram vector. The sum scalogram vector may be normalized, e.g.,such that the scale having the highest energy has a value of 1.

FIG. 27 depicts exemplary steps for determining respiration informationfrom the sum scalogram vector. It will be understood that the order ofthe steps of FIG. 27 may be modified, steps may be omitted, andadditional steps may be added. At step 2702, a threshold may becalculated for the sum scalogram vector. The threshold may be calculatedin any suitable manner. In an exemplary embodiment, the threshold may bebased on the maximum value in the combined sum scalogram, e.g., at 50%of the maximum value. At step 2704, processor 314 may identify candidatescales from the sum scalogram vector based on the threshold. Forexample, each local maxima of the sum scalogram vector may be comparedto threshold. Only the local maxima that exceed the threshold may becandidate scales. Any local maxima that do not exceed the threshold maybe disregarded.

At step 2706, processor 314 may select the candidate scale to be used todetermine respiration information. It will be understood that thecandidate scale may be selected in any suitable manner. In an exemplaryembodiment, the selected scale may be the lowest scale value thatexceeds the threshold. At step 2708, respiration information such asrespiration rate may be calculated from the selected scale. In theexemplary embodiment described above the scales may correspond to thecharacteristic frequency of the corresponding wavelets, e.g., acharacteristic frequency range of 0.05 Hz-1.0 Hz. A scale value of zeromay correspond to a minimum pulse period (e.g., corresponding to acharacteristic frequency of 1.0 Hz for the corresponding wavelet) whilea scale value of 60 may correspond to a maximum pulse period (e.g.,corresponding to a characteristic frequency of 0.05 Hz for thecorresponding wavelet). The pulse period for the selected scale may becalculated based on the maximum or minimum pulse period, the scalenumber, and the scale interval. For example, a scale value of 50 maycorrespond to a pulse period of 4.73 seconds, which may be equivalent to12.66 breaths per minute.

In another embodiment, respiration information may be calculated basedon identifying suitable portions (e.g., peaks) of the combinedautocorrelation signal. At steps 2240 and 2242, processor 314 maydetermine respiration information directly from the combinedautocorrelation sequence. Respiration information may be determined fromthe combined autocorrelation sequence in any suitable manner. In anexemplary embodiment, respiration information may be determined from thecombined autocorrelation sequence based on the steps of FIG. 28. At step2802, processor 314 may set parameters for determining respirationinformation from the combined autocorrelation sequence. Exemplarycombined autocorrelation sequences are depicted in FIG. 29, FIG. 30, andFIG. 31. The combined autocorrelation sequence may be symmetric aboutthe point where the sequence directly overlaps with itself, i.e., theright side and left side of the combined autocorrelation sequence may bethe same. Determination of respiration information may be simplified bylooking only at one side of the combined autocorrelation sequence, e.g.,the right side as is depicted in FIG. 29, FIG. 30, and FIG. 31. Theabscissa of each of FIG. 29, FIG. 30, and FIG. 31 may be in units oftime, and the ordinate may be in units of amplitude.

FIG. 29 depicts an exemplary combined autocorrelation sequence 2902 thatmay be directly analyzed to determine respiration information. Thecombined autocorrelation sequence 2902 may have a series of peaks thatappear at regular intervals and decrease in magnitude over time. Line2904 may be indicative of a rate of decay of the combinedautocorrelation sequence 2902 and may define an expected autocorrelationenvelope. The peaks of the combined autocorrelation sequence 2902 mayroughly align with the rate of decay, which may be indicative of asignal from which respiration information may be accurately determined.

FIG. 30 depicts an exemplary combined autocorrelation sequence 3002 thatmay be directly analyzed to determine respiration information. Thecombined autocorrelation sequence 3002 may have a series of peaks thatappear at regular intervals and decrease in magnitude over time. Line3004 may be indicative of a baseline rate of decay of a combinedautocorrelation sequence and may define an expected autocorrelationenvelope, which does not correspond to the rate of decay of combinedautocorrelation sequence 3002. The lower magnitude peaks are indicativeof a signal that does not have significant periodic characteristics overthe analysis window, and may not be suitable for determining respirationinformation. It will be understood that there may be many reasons thatthe underlying signal does not display significant periodiccharacteristics, for example the signal may have a significant source ofnonstationarity, e.g., as a result of step change, phase irregularity,or a gradual change in respiration rate.

FIG. 31 depicts an exemplary combined autocorrelation sequence 3102 thatmay be directly analyzed to determine respiration information. Thecombined autocorrelation sequence 3102 may have a series of peaks thatappear at regular intervals and decrease in magnitude over time. Line3104 may be indicative of a baseline rate of decay of a combinedautocorrelation sequence and may define an expected autocorrelationenvelope, which may correspond to a number of the peaks of combinedautocorrelation sequence 3102. Other peaks, which are indicated bypoints 3106 and 3108, may be indicative of harmonic components ofcombined autocorrelation sequence 3102.

Referring again to FIG. 28, at step 2802 processor 314 may setparameters for determining respiration information from the combinedautocorrelation sequence. It will be understood that there are numerousparameters that may be set such as thresholds and relevant ranges ofinterest. It will also be understood that such parameters may be set inany suitable manner to improve the determination of respirationinformation. In one exemplary embodiment a threshold may be set for themagnitude of the peaks that may be considered to determine respirationinformation. A threshold may be set such that peaks corresponding toharmonics (e.g., peaks 3106 and 3108 of FIG. 31) and low magnitude peaksof signals that are irregular or non-periodic (e.g., signal 3002 of FIG.30) are ignored for determining respiration information. Exemplarythresholds are depicted as threshold 2906 in FIG. 29, threshold 3006 inFIG. 30, and threshold 3110 in FIG. 31. The threshold may correspond toa maximum amplitude as depicted by thresholds 2906, 3006, and 3110, maybe values that may be compared to amplitude differences (e.g., in a peakto trough embodiment described herein, or may be determined in any othersuitable manner. Harmonic peaks may not correspond to respirationinformation (respiration rate), while irregular or non-periodic signalsmay not have a signal that accurately captures respiration information.Setting a threshold may avoid choosing such peaks. Other amplitudethresholds may also be set, such as a difference threshold for twoconsecutive peaks. For example, a difference threshold may require thatfor a peak to be considered for purposes of determining respirationinformation, the amplitude of the peak must exceed the amplitude of thesubsequent peak by at least a threshold, e.g., 70%. In another exemplaryembodiment a difference threshold may be set based on the expected decaycharacteristics of the combined autocorrelation sequence.

Another exemplary parameter may be a relevant range of interest, e.g. onthe time scale of the combined autocorrelation sequence. The peaks ofthe combined autocorrelation sequence may correspond to instances wherethe underlying signal (e.g., a morphology metric signal) has beentranslated in time and is similar to itself, which may demonstrate aperiodic or regular signal. Thus the time between peaks that arerepresentative of the respiration information may be equivalent to theperiod of the respiration, which may be utilized to determinerespiration rate (e.g. the frequency of respiration). In an exemplaryembodiment a range of interest may be set to correspond to a respirationrate, such as from 4 to 40 breaths per minute. An exemplary range ofinterest is depicted as range of interest 2908 in FIG. 29, range ofinterest 3008 in FIG. 30, and range of interest 3112 in FIG. 31. It willbe understood that the range of interest may be set in any suitablemanner. For example, in another embodiment the range of interest may bebased upon a maximum time between any two consecutive peaks.

Referring again to FIG. 28, at step 2804 processor 314 may identifyharmonics and outliers. As was discussed above, a threshold may excludemany harmonic or outlying values because the magnitude of theautocorrelation is less likely to exceed the threshold at such points.In another exemplary embodiment harmonics may be identified based onexpected harmonic values. A largest peak of the combined autocorrelationsequence may be likely to correspond to respiration information. Otherpeaks may occur at intervals that would be expected to be harmonics,e.g., at approximately 50% of the time of the largest peak. For example,in FIG. 31 a largest peak may correspond to point 3114. Other peaks atpoints 3106 and 3108 may approximately correspond to 50% of the periodassociated with largest peak 3114 and may be classified as likelyharmonic peaks. Any harmonic peaks or other outliers that are identifiedmay be excluded from consideration as potential selected peaks.

At step 2806 processor 314 may select a peak associated with arespiration rate. It will be understood that selecting the peak may beperformed in any suitable manner, such as selecting the first peak tothe right of the vertical axis or a maximum peak value, e.g., peak 2910in FIG. 29. In another exemplary embodiment, selecting the peak may bebased on any parameters that were set in step 2802 such as a thresholdand a range of interest. For example, peak 2910 in FIG. 29 may exceedthreshold 2906 and be within a range of interest 2908, peak 3114 mayexceed threshold 3110 and be within range of interest 3112, and theremay be no peak of combined autocorrelation sequence 3002 that exceedsthreshold 2906 within range of interest 2908. Selecting the peak withina range of interest may be performed in any suitable manner, such asselecting the first peak within the range of interest or selecting thepeak with the largest amplitude.

In another exemplary embodiment, analysis of the peaks may be based onthe peak to trough amplitude of the peak. The peak to trough amplitudemay be based on any suitable points. In one exemplary embodiment, a peakto trough amplitude may be based on a selected peak and a precedingtrough, as is depicted in by amplitude 2914 between peak 2910 and trough2912 in FIG. 29. In another exemplary embodiment, a peak to troughamplitude may be based on a selected peak and a subsequent trough, as isdepicted in by amplitude 2918 between peak 2910 and trough 2916 in FIG.29. In another exemplary embodiment, a peak to trough amplitude may bebased on a selected peak and a midpoint trough associated with the peak,as is depicted in by amplitude 2922 between peak 2910 and midpointtrough 2920 in FIG. 29. Once the peak to trough amplitude is determinedfor the peak, selecting a peak corresponding to respiration informationmay be performed in any suitable manner, such as comparing the amplitudeof each peak within a range of interest to a threshold, and selecting apeak based on amplitude or relative position.

At step 2808, processor 314 may determine respiration information suchas respiration rate based on the selected peak. It will be understoodthat respiration information may be determined in any suitable manner.In an exemplary embodiment the time value associated with the selectedpeak may be related to the period for respiration, which may be used todetermine respiration information such as respiration rate. In anotherexemplary embodiment, one or more time differences between a selectedpeak and one or more other peaks may be related to the period forrespiration, which may be used to determine respiration information suchas respiration rate. Processor 314 may also calculate a confidence valueassociated with the determined respiration information. For example, abest fit line may be generated for the peaks of the combinedautocorrelation sequence. The confidence value may be determined basedon the variability of the best fit line in any suitable manner, such asbased on a R² residual sum. In another exemplary embodiment processor314 may assess the distribution of the time between adjacent peaks ofthe combined autocorrelation sequence. A higher variability for thedistribution may be indicative of a lower confidence value.

Referring again to FIG. 22, the calculated respiration information(e.g., respiration rate) may be filtered at step 2244. A combinedautocorrelation metric may be calculated for the combinedautocorrelation sequence in the same manner as the individualautocorrelation sequences, e.g., based on four local minima values asdescribed herein. The filter may utilize the combined autocorrelationmetric to determine how much weight to place on the value of the currentrespiration information versus a previous value of filtered respirationinformation. The more regular the combined autocorrelation sequence, themore emphasis may be placed on the current respiration information. Thefiltered respiration information may be calculated as follows:R _(filt) =R _(wt) *R _(new)+(1−R _(wt))*R′ _(filt)where:R_(filt)=filtered respiration information;R_(wt)=combined autocorrelation metric;R_(new)=calculated respiration information; andR′_(filt)=Previous filtered respiration information.

It will be recognized that filtering the value of the currentrespiration information with previous values of respiration informationmay be performed in any suitable manner. For example, a combinedautocorrelation value may be calculated utilizing local maxima values orother parameters of the combined autocorrelation signal.

The combined autocorrelation metric may also be utilized to calculate anage for the filtered respiration information in any suitable manner. Forexample, the age may be calculated based on the combined autocorrelationage (calculated above) and the previous filtered respiration age asfollows:R _(age) =R _(wt)*CombinedAge+(1−R _(wt))*R′ _(age)where:R_(age)=filtered respiration age;R_(wt)=combined autocorrelation metric;CombinedAge=age of combined autocorrelation sequence;R′_(filt)=previous filtered respiration age.

Processor 314 may communicate information to post-processor 316, such asthe filtered respiration information, filtered respiration age, and thetime ratio. In an exemplary embodiment, post-processor 316 may calculatea display value from the value of current filtered respirationinformation and values for previous filtered respiration information.

In an exemplary embodiment, post-processor 316 may receive the filteredrespiration information, filtered respiration age, and time ratio fromprocessor 314. Post-processor 316 may also receive period variabilityand amplitude variability values from pre-processor 312. Post-processor316 may generate display respiration information in any suitable manner.For example, display information may be based on the currently receivedinformation. In another example, the display information may be based onthe received information as well as previously received information. Inan exemplary embodiment, post-processor 316 may calculate the displayrespiration information from the filtered respiration information forthe current analysis window and filtered respiration information for oneor more previous analysis windows, e.g., the five previous analysiswindows. A weight for each analysis window may be calculated from theperiod variability and amplitude variability for that analysis window asfollows:

${w(k)} = \frac{{P_{var}(k)} - {A_{var}(k)}}{2}$w(k) = 1 − min (w(k), 1) w(k) = w(k)²⁰where:P_(var)=period variability;A_(var)=amplitude variability; andk=analysis window of the N total analysis windows, in ascending orderfrom most recent analysis window to oldest analysis window.

Once a weight is calculated for each respective analysis window, thedisplay value can be calculated by combining the values for the filteredrespiration information based on the calculated weights as follows:

${{Display}\mspace{14mu}{Value}} = \frac{\sum\limits_{k = 0}^{N - 1}{{w(k)}{R_{filt}(k)}}}{\sum\limits_{k = 0}^{N - 1}{w(k)}}$where:w(k)=weight for the kth analysis window;R_(filt)=filtered respiration information for the kth analysis window;andN=total number of analysis windows in display value calculation.

The display value may be displayed, e.g., at display 28 of displaymonitor 26 as a respiration rate value.

Post-processor 316 may also calculate an age for the display value basedon the weight and filtered respiration age associated with each analysiswindow as follows:

${{Display}\mspace{14mu}{Age}} = \frac{\sum\limits_{k = 0}^{N - 1}{{w(k)}( {{R_{age}(k)} + {5*k}} )}}{\sum\limits_{k = 0}^{N - 1}{w(k)}}$where:w(k)=weight for the kth analysis window;R_(age)=filtered respiration age for the kth analysis window;N=total number of analysis windows in display value calculation.

The 5*k term takes into account that the filtered respiration age valuesassociated with previous analysis windows have aged since the valueswere initially determined. It will be recognized that the display valueand display age may be calculated in any suitable manner.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications may be made by those skilled in theart without departing from the scope of this disclosure. The abovedescribed embodiments are presented for purposes of illustration and notof limitation. The present disclosure also can take many forms otherthan those explicitly described herein. Accordingly, it is emphasizedthat this disclosure is not limited to the explicitly disclosed methods,systems, and apparatuses, but is intended to include variations to andmodifications thereof, which are within the spirit of the followingclaims.

What is claimed is:
 1. A patient monitoring system, the systemcomprising: a sensor interface configured to receive aphotoplethysmograph signal of a patient, the photoplethysmograph signalcomprising a plurality of pulse waves; processing equipment configuredto: sample the photoplethysmograph signal; determine two successivereference points on the sampled photoplethysmograph signal,corresponding to two successive pulses; locate a maximum value of thesampled photoplethysmograph signal between the two successive referencepoints; select a fiducial point located a predetermined number ofsamples before or after the located maximum value and between the twosuccessive points; and calculate a respiration rate based at least inpart on the selected fiducial point.
 2. The system of claim 1, furthercomprising memory coupled to the processing equipment, wherein thememory is configured to store fiducial information comprising thepredetermined number of samples, and wherein the processing equipment isfurther configured to access the fiducial information.
 3. The system ofclaim 1, wherein the predetermined number of samples is 16 samples. 4.The system of claim 1, wherein the processing equipment is furtherconfigured to create a fiducial signal based at least in part on theselected fiducial point.
 5. The system of claim 1, wherein each of thetwo successive reference points of the photoplethysmograph signal is ofa type selected from the group consisting of a zero, a first derivativemaximum, a first derivative zero, a first derivative minimum, a secondderivative maximum, a second derivative minimum, a second derivativezero, and any combination thereof.
 6. The system of claim 1, wherein thephotoplethysmograph comprises a plurality of pulse waves, and whereineach of the two successive reference points corresponds to a singlepulse wave of the plurality of pulse waves.
 7. The system of claim 1,wherein the predetermined number of samples is predetermined based atleast in part on a pulse period.
 8. The system of claim 7, wherein thepredetermined number of samples corresponds to a time interval of 10% ofthe pulse period.